Neural network unit with output buffer feedback for performing recurrent neural network computations

ABSTRACT

A neural network unit has at least one RAM, an output buffer and an array of neural processing units that: read first time step context layer node values from the output buffer; read second time step input layer node values from the RAM; generate second time step hidden layer node values based on the read input and context layer node values; output the hidden layer node values to the output buffer rather than to the RAM; read the hidden layer node values from the output buffer; generate second time step context layer node values based on the read hidden layer node values; output the context layer node values to the output buffer rather than to the RAM; generate output layer node values using the hidden layer node values; write the output layer node values to the RAM; and repeat for a sequence of time steps.

CROSS REFERENCE TO RELATED APPLICATION(S)

This application claims priority based on the following U.S. ProvisionalApplications, each of which is hereby incorporated by reference in itsentirety.

Ser. No. Filing Date Title VAS.3039 Oct. 8, 2015 PROCESSOR WITH NEURALNETWORK UNIT VAS.3060 Dec. 2, 2015 PROCESSOR WITH VARIABLE RATEEXECUTION UNIT VAS.3069 Feb. 4, 2016 MECHANISM FOR COMMUNICATION BETWEENARCHITECTURAL PROGRAM RUNNING ON PROCESSOR AND NON-ARCHITECTURAL PROGRAMRUNNING ON EXECUTION UNIT OF THE PROCESSOR REGARDING SHARED RESOURCE;NEURAL NETWORK UNIT WITH OUTPUT BUFFER FEEDBACK AND MASKING CAPABILITY,AND THAT PERFORMS CONCURRENT LSTM CELL CALCULATIONS, AND WITH OUTPUTBUFFER FEEDBACK FOR PERFORMING RECURRENT NEURAL NETWORK COMPUTATIONSThis application is related to the following concurrently-filed U.S.Non-Provisional Applications, each of which is hereby incorporated byreference in its entirety.

Ser. No. Title VAS.3039 NEURAL NETWORK UNIT WITH NEURAL MEMORY AND ARRAYOF NEURAL PROCESSING UNITS THAT COLLECTIVELY SHIFT ROW OF DATA RECEIVEDFROM NEURAL MEMORY VAS.3040 TRI-CONFIGURATION NEURAL NETWORK UNITVAS.3049 PROCESSOR WITH ARCHITECTURAL NEURAL NETWORK EXECUTION UNITVAS.3050 NEURAL NETWORK UNIT WITH NEURAL PROCESSING UNITS DYNAMICALLYCONFIGURABLE TO PROCESS MULTIPLE DATA SIZES VAS.3051 NEURAL PROCESSINGUNIT THAT SELECTIVELY WRITES BACK TO NEURAL MEMORY EITHER ACTIVATIONFUNCTION OUTPUT OR ACCUMULATOR VALUE VAS.3052 NEURAL NETWORK UNIT WITHSHARED ACTIVATION FUNCTION UNITS VAS.3053 NEURAL NETWORK UNIT EMPLOYINGUSER-SUPPLIED RECIPROCAL FOR NORMALIZING AN ACCUMULATED VALUE VAS.3059PROCESSOR WITH VARIABLE RATE EXECUTION UNIT VAS.3060 MECHANISM FORCOMMUNICATION BETWEEN ARCHITECTURAL PROGRAM RUNNING ON PROCESSOR ANDNON-ARCHITECTURAL PROGRAM RUNNING ON EXECUTION UNIT OF THE PROCESSORREGARDING SHARED RESOURCE VAS.3062 DIRECT EXECUTION BY AN EXECUTION UNITOF A MICRO- OPERATION LOADED INTO AN ARCHITECTURAL REGISTER FILE BY ANARCHITECTURAL INSTRUCTION OF A PROCESSOR VAS.3063 MULTI-OPERATION NEURALNETWORK UNIT VAS.3064 NEURAL NETWORK UNIT THAT PERFORMS CONVOLUTIONSUSING COLLECTIVE SHIFT REGISTER AMONG ARRAY OF NEURAL PROCESSING UNITSVAS.3065 NEURAL NETWORK UNIT WITH PLURALITY OF SELECTABLE OUTPUTFUNCTIONS VAS.3066 NEURAL NETWORK UNIT THAT PERFORMS STOCHASTIC ROUNDINGVAS.3067 APPARATUS EMPLOYING USER-SPECIFIED BINARY POINT FIXED POINTARITHMETIC VAS.3068 PROCESSOR WITH HYBRID COPROCESSOR/EXECUTION UNITNEURAL NETWORK UNIT VAS.3069 NEURAL NETWORK UNIT WITH OUTPUT BUFFERFEEDBACK AND MASKING CAPABILITY VAS.3075 NEURAL NETWORK UNIT THATPERFORMS CONCURRENT LSTM CELL CALCULATIONS VAS.3076 NEURAL NETWORK UNITWITH OUTPUT BUFFER FEEDBACK FOR PERFORMING RECURRENT NEURAL NETWORKCOMPUTATIONS VAS.3078 NEURAL NETWORK UNIT WITH NEURAL MEMORY AND ARRAYOF NEURAL PROCESSING UNITS AND SEQUENCER THAT COLLECTIVELY SHIFT ROW OFDATA RECEIVED FROM NEURAL MEMORY VAS.3079 NEURAL NETWORK UNIT WITHOUTPUT BUFFER FEEDBACK AND MASKING CAPABILITY WITH PROCESSING UNITGROUPS THAT OPERATE AS RECURRENT NEURAL NETWORK LSTM CELLS

BACKGROUND

Recently, there has been a resurgence of interest in artificial neuralnetworks (ANN), and such research has commonly been termed deeplearning, computer learning and similar terms. The increase ingeneral-purpose processor computation power has given rise to therenewed interest that waned a couple of decades ago. Recent applicationsof ANNs have included speech and image recognition, along with others.There appears to be an increasing demand for improved performance andefficiency of computations associated with ANNs.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a processor that includes aneural network unit (NNU).

FIG. 2 is a block diagram illustrating a NPU of FIG. 1.

FIG. 3 is a block diagram illustrating an embodiment of the arrangementof the N mux-regs of the N NPUs of the NNU of FIG. 1 to illustrate theiroperation as an N-word rotater, or circular shifter, for a row of datawords received from the data RAM of FIG. 1.

FIG. 4 is a table illustrating a program for storage in the programmemory of and execution by the NNU of FIG. 1.

FIG. 5 is a timing diagram illustrating the execution of the program ofFIG. 4 by the NNU.

FIG. 6A is a block diagram illustrating the NNU of FIG. 1 to execute theprogram of FIG. 4.

FIG. 6B is a flowchart illustrating operation of the processor of FIG. 1to perform an architectural program that uses the NNU to performmultiply-accumulate-activation function computations classicallyassociated with neurons of hidden layers of an artificial neural networksuch as performed by the program of FIG. 4.

FIG. 7 is a block diagram illustrating a NPU of FIG. 1 according to analternate embodiment.

FIG. 8 is a block diagram illustrating a NPU of FIG. 1 according to analternate embodiment.

FIG. 9 is a table illustrating a program for storage in the programmemory of and execution by the NNU of FIG. 1.

FIG. 10 is a timing diagram illustrating the execution of the program ofFIG. 9 by the NNU.

FIG. 11 is a block diagram illustrating an embodiment of the NNU of FIG.1 is shown. In the embodiment of FIG. 11, a neuron is split into twoportions, the activation function unit portion and the ALU portion(which also includes the shift register portion), and each activationfunction unit portion is shared by multiple ALU portions.

FIG. 12 is a timing diagram illustrating the execution of the program ofFIG. 4 by the NNU of FIG. 11.

FIG. 13 is a timing diagram illustrating the execution of the program ofFIG. 4 by the NNU of FIG. 11.

FIG. 14 is a block diagram illustrating a move to neural network (MTNN)architectural instruction and its operation with respect to portions ofthe NNU of FIG. 1.

FIG. 15 is a block diagram illustrating a move from neural network(MFNN) architectural instruction and its operation with respect toportions of the NNU of FIG. 1.

FIG. 16 is a block diagram illustrating an embodiment of the data RAM ofFIG. 1.

FIG. 17 is a block diagram illustrating an embodiment of the weight RAMof FIG. 1 and a buffer.

FIG. 18 is a block diagram illustrating a dynamically configurable NPUof FIG. 1.

FIG. 19 is a block diagram illustrating an embodiment of the arrangementof the 2N mux-regs of the N NPUs of the NNU of FIG. 1 according to theembodiment of FIG. 18 to illustrate their operation as a rotater for arow of data words received from the data RAM of FIG. 1.

FIG. 20 is a table illustrating a program for storage in the programmemory of and execution by the NNU of FIG. 1 having NPUs according tothe embodiment of FIG. 18.

FIG. 21 is a timing diagram illustrating the execution of the program ofFIG. 20 by the NNU that includes NPUs of FIG. 18 operating in a narrowconfiguration.

FIG. 22 is a block diagram illustrating the NNU of FIG. 1 including theNPUs of FIG. 18 to execute the program of FIG. 20.

FIG. 23 is a block diagram illustrating a dynamically configurable NPUof FIG. 1 according to an alternate embodiment.

FIG. 24 is a block diagram illustrating an example of data structuresused by the NNU of FIG. 1 to perform a convolution operation.

FIG. 25 is a flowchart illustrating operation of the processor of FIG. 1to perform an architectural program that uses the NNU to perform aconvolution of the convolution kernel with the data array of FIG. 24.

FIG. 26A is a program listing of an NNU program that performs aconvolution of a data matrix with the convolution kernel of FIG. 24 andwrites it back to the weight RAM.

FIG. 26B is a block diagram illustrating certain fields of the controlregister of the NNU of FIG. 1 according to one embodiment.

FIG. 27 is a block diagram illustrating an example of the weight RAM ofFIG. 1 populated with input data upon which a pooling operation isperformed by the NNU of FIG. 1.

FIG. 28 is a program listing of an NNU program that performs a poolingoperation of the input data matrix of FIG. 27 and writes it back to theweight RAM.

FIG. 29A is a block diagram illustrating an embodiment of the controlregister of FIG. 1.

FIG. 29B is a block diagram illustrating an embodiment of the controlregister of FIG. 1 according to an alternate embodiment.

FIG. 29C is a block diagram illustrating an embodiment of the reciprocalof FIG. 29A stored as two parts according to one embodiment.

FIG. 30 is a block diagram illustrating in more detail an embodiment ofan AFU of FIG. 2.

FIG. 31 is an example of operation of the AFU of FIG. 30.

FIG. 32 is a second example of operation of the AFU of FIG. 30.

FIG. 33 is a third example of operation of the AFU of FIG. 30.

FIG. 34 is a block diagram illustrating the processor of FIG. 1 and inmore detail portions of the NNU of FIG. 1.

FIG. 35 is a block diagram illustrating a processor that includes avariable rate NNU.

FIG. 36A is a timing diagram illustrating an example of operation of theprocessor with the NNU operating in normal mode, i.e., at the primaryclock rate.

FIG. 36B is a timing diagram illustrating an example of operation of theprocessor with the NNU operating in relaxed mode, i.e., at a rate thatis less than the primary clock rate.

FIG. 37 is a flowchart illustrating operation of the processor of FIG.35.

FIG. 38 is a block diagram illustrating the sequence of the NNU in moredetail.

FIG. 39 is a block diagram illustrating certain fields of the controland status register of the NNU.

FIG. 40 is a block diagram illustrating an example of an Elman RNN.

FIG. 41 is a block diagram illustrating an example of the layout of datawithin the data RAM and weight RAM of the NNU as it performscalculations associated with the Elman RNN of FIG. 40.

FIG. 42 is a table illustrating a program for storage in the programmemory of and execution by the NNU to accomplish an Elman RNN and usingdata and weights according to the arrangement of FIG. 41.

FIG. 43 is a block diagram illustrating an example of an Jordan RNN.

FIG. 44 is a block diagram illustrating an example of the layout of datawithin the data RAM and weight RAM of the NNU as it performscalculations associated with the Jordan RNN of FIG. 43.

FIG. 45 is a table illustrating a program for storage in the programmemory of and execution by the NNU to accomplish a Jordan RNN and usingdata and weights according to the arrangement of FIG. 44.

FIG. 46 is a block diagram illustrating an embodiment of an LSTM cell.

FIG. 47 is a block diagram illustrating an example of the layout of datawithin the data RAM and weight RAM of the NNU as it performscalculations associated with a layer of LSTM cells of FIG. 46.

FIG. 48 is a table illustrating a program for storage in the programmemory of and execution by the NNU to accomplish computations associatedwith an LSTM cell layer and using data and weights according to thearrangement of FIG. 47.

FIG. 49 is a block diagram illustrating an NNU embodiment with outputbuffer masking and feedback capability within NPU groups.

FIG. 50 is a block diagram illustrating an example of the layout of datawithin the data RAM, weight RAM and output buffer of the NNU of FIG. 49as it performs calculations associated with a layer of LSTM cells ofFIG. 46.

FIG. 51 is a table illustrating a program for storage in the programmemory of and execution by the NNU of FIG. 49 to accomplish computationsassociated with an LSTM cell layer and using data and weights accordingto the arrangement of FIG. 50.

FIG. 52 is a block diagram illustrating an NNU embodiment with outputbuffer masking and feedback capability within NPU groups and whichemploys shared AFUs.

FIG. 53 is a block diagram illustrating an example of the layout of datawithin the data RAM, weight RAM and output buffer of the NNU of FIG. 49as it performs calculations associated with a layer of LSTM cells ofFIG. 46 according to an alternate embodiment.

FIG. 54 is a table illustrating a program for storage in the programmemory of and execution by the NNU of FIG. 49 to accomplish computationsassociated with an LSTM cell layer and using data and weights accordingto the arrangement of FIG. 53.

FIG. 55 is a block diagram illustrating portions of an NPU according toan alternate embodiment.

FIG. 56 is a block diagram illustrating an example of the layout of datawithin the data RAM and weight RAM of the NNU as it performscalculations associated with the Jordan RNN of FIG. 43 but employing thebenefits afforded by the embodiments of FIG. 55.

FIG. 57 is a table illustrating a program for storage in the programmemory of and execution by the NNU to accomplish a Jordan RNN and usingdata and weights according to the arrangement of FIG. 56.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Processor with Architectural Neural Network Unit

Referring now to FIG. 1, a block diagram illustrating a processor 100that includes a neural network unit (NNU) 121 is shown. The processor100 includes an instruction fetch unit 101, an instruction cache 102,and instruction translator 104, a rename unit 106, reservation stations108, media registers 118, general purpose registers (GPR) 116, executionunits 112 other than the NNU 121, and a memory subsystem 114.

The processor 100 is an electronic device that functions as a centralprocessing unit (CPU) on an integrated circuit. The processor 100receives digital data as input, processes the data according toinstructions fetched from a memory, and generates results of operationsprescribed by the instructions as output. The processor 100 may beemployed in a desktop, mobile, or tablet computer, and is employed foruses such as computation, text editing, multimedia display, and Internetbrowsing. The processor 100 may also be disposed in an embedded systemto control a wide variety of devices including appliances, mobiletelephones, smart phones, automobiles and industrial control devices. ACPU is the electronic circuits (i.e., “hardware”) that execute theinstructions of a computer program (also known as a “computerapplication” or “application”) by performing operations on data thatinclude arithmetic operations, logical operations, and input/outputoperations. An integrated circuit (IC) is a set of electronic circuitsfabricated on a small piece of semiconductor material, typicallysilicon. An IC is also referred to as a chip, a microchip, or a die.

The instruction fetch unit 101 controls the fetching of architecturalinstructions 103 from system memory (not shown) into the instructioncache 102. The instruction fetch unit 101 provides a fetch address tothe instruction cache 102 that specifies a memory address at which theprocessor 100 fetches a cache line of architectural instruction bytesinto the instruction cache 102. The fetch address is based on thecurrent value of the instruction pointer (not shown), or programcounter, of the processor 100. Normally, the program counter isincremented sequentially by the size of an instruction unless a controlinstruction is encountered in the instruction stream, such as a branch,call or return instruction, or an exception condition occurs, such as aninterrupt, trap, exception or fault, in which case the program counteris updated with a non-sequential address, such as a branch targetaddress, return address or exception vector. Generally speaking, theprogram counter is updated in response to the execution of instructionsby the execution units 112/121. The program counter may also be updatedin response to detection of an exception condition such as theinstruction translator 104 encountering an instruction 103 that is notdefined by the instruction set architecture of the processor 100.

The instruction cache 102 caches the architectural instructions 103fetched from a system memory that is coupled to the processor 100. Thearchitectural instructions 103 include a move to neural network (MTNN)instruction and a move from neural network (MFNN) instruction, which aredescribed in more detail below. In one embodiment, the architecturalinstructions 103 are instructions of the x86 instruction setarchitecture (ISA), with the addition of the MTNN and MFNN instructions.In the context of the present disclosure, an x86 ISA processor as aprocessor that generates the same results at the instruction setarchitecture level that an Intel® 80386® processor generates when itexecutes the same machine language instructions. However, otherembodiments contemplate other instruction set architectures, such asAdvanced RISC Machines (ARM)®, Sun SPARC®, or PowerPC®. The instructioncache 102 provides the architectural instructions 103 to the instructiontranslator 104, which translates the architectural instructions 103 intomicroinstructions 105.

The microinstructions 105 are provided to the rename unit 106 andeventually executed by the execution units 112/121. Themicroinstructions 105 implement the architectural instructions.Preferably, the instruction translator 104 includes a first portion thattranslates frequently executed and/or relatively less complexarchitectural instructions 103 into microinstructions 105. Theinstruction translator 104 also includes a second portion that includesa microcode unit (not shown). The microcode unit includes a microcodememory that holds microcode instructions that implement complex and/orinfrequently used instructions of the architectural instruction set. Themicrocode unit also includes a microsequencer that provides anon-architectural micro-program counter (micro-PC) to the microcodememory. Preferably, the microcode instructions are translated by amicrotranslator (not shown) into the microinstructions 105. A selectorselects the microinstructions 105 from either the first portion or thesecond portion for provision to the rename unit 106, depending uponwhether or not the microcode unit currently has control.

The rename unit 106 renames architectural registers specified in thearchitectural instructions 103 to physical registers of the processor100. Preferably, the processor 100 includes a reorder buffer (notshown). The rename unit 106 allocates, in program order, an entry in thereorder buffer for each microinstruction 105. This enables the processor100 to retire the microinstructions 105, and their correspondingarchitectural instructions 103, in program order. In one embodiment, themedia registers 118 are 256 bits wide and the GPR 116 are 64 bits wide.In one embodiment, the media registers 118 are x86 media registers, suchas Advanced Vector Extensions (AVX) registers.

In one embodiment, each entry in the reorder buffer includes storage forthe result of the microinstruction 105; additionally, the processor 100includes an architectural register file that includes a physicalregister for each of the architectural registers, e.g., the mediaregisters 118 and the GPR 116 and other architectural registers.(Preferably, there are separate register files for the media registers118 and GPR 116, for example, since they are different sizes.) For eachsource operand of a microinstruction 105 that specifies an architecturalregister, the rename unit populates the source operand field in themicroinstruction 105 with the reorder buffer index of the newest oldermicroinstruction 105 that writes to the architectural register. When theexecution unit 112/121 completes execution of the microinstruction 105,it writes the result to the microinstruction's 105 reorder buffer entry.When the microinstruction 105 retires, a retire unit (not shown) writesthe result from the microinstruction's reorder buffer entry to theregister of the physical register file associated with the architecturaldestination register specified by the retiring microinstruction 105.

In another embodiment, the processor 100 includes a physical registerfile that includes more physical registers than the number ofarchitectural registers, but does not include an architectural registerfile, and the reorder buffer entries do not include result storage.(Preferably, there are separate physical register files for the mediaregisters 118 and GPR 116, for example, since they are different sizes.)The processor 100 also includes a pointer table with an associatedpointer for each architectural register. For the operand of amicroinstruction 105 that specifies an architectural register, therename unit populates the destination operand field in themicroinstruction 105 with a pointer to a free register in the physicalregister file. If no registers are free in the physical register file,the rename unit 106 stalls the pipeline. For each source operand of amicroinstruction 105 that specifies an architectural register, therename unit populates the source operand field in the microinstruction105 with a pointer to the register in the physical register fileassigned to the newest older microinstruction 105 that writes to thearchitectural register. When the execution unit 112/121 completesexecution of the microinstruction 105, it writes the result to aregister of the physical register file pointed to by themicroinstruction's 105 destination operand field. When themicroinstruction 105 retires, the retire unit copies themicroinstruction's 105 destination operand field value to the pointer inthe pointer table associated with the architectural destination registerspecified by the retiring microinstruction 105.

The reservation stations 108 hold microinstructions 105 until they areready to be issued to an execution unit 112/121 for execution. Amicroinstruction 105 is ready to be issued when all of its sourceoperands are available and an execution unit 112/121 is available toexecute it. The execution units 112/121 receive register source operandsfrom the reorder buffer or the architectural register file in the firstembodiment or from the physical register file in the second embodimentdescribed above. Additionally, the execution units 112/121 may receiveregister source operands directly from the execution units 112/121 viaresult forwarding buses (not shown). Additionally, the execution units112/121 may receive from the reservation stations 108 immediate operandsspecified by the microinstructions 105. As discussed in more detailbelow, the MTNN and MFNN architectural instructions 103 include animmediate operand that specifies a function to be performed by the NNU121 that is provided in one of the one or more microinstructions 105into which the MTNN and MFNN architectural instructions 103 aretranslated.

The execution units 112 include one or more load/store units (not shown)that load data from the memory subsystem 114 and store data to thememory subsystem 114. Preferably, the memory subsystem 114 includes amemory management unit (not shown), which may include, e.g., translationlookaside buffers and a tablewalk unit, a level-1 data cache (and theinstruction cache 102), a level-2 unified cache, and a bus interfaceunit that interfaces the processor 100 to system memory. In oneembodiment, the processor 100 of FIG. 1 is representative of aprocessing core that is one of multiple processing cores in a multi-coreprocessor that share a last-level cache memory.

The execution units 112 may also include integer units, media units,floating-point units and a branch unit.

The NNU 121 includes a weight random access memory (RAM) 124, a data RAM122, N neural processing units (NPUs) 126, a program memory 129, asequencer 128 and control and status registers 127. The NPUs 126function conceptually as neurons in a neural network. The weight RAM124, data RAM 122 and program memory 129 are all writable and readablevia the MTNN and MFNN architectural instructions 103, respectively. Theweight RAM 124 is arranged as W rows of N weight words, and the data RAM122 is arranged as D rows of N data words. Each data word and eachweight word is a plurality of bits, preferably 8 bits, 9 bits, 12 bitsor 16 bits. Each data word functions as the output value (also sometimesreferred to as an activation) of a neuron of the previous layer in thenetwork, and each weight word functions as a weight associated with aconnection coming into a neuron of the instant layer of the network.Although in many uses of the NNU 121 the words, or operands, held in theweight RAM 124 are in fact weights associated with a connection cominginto a neuron, it should be understood that in other uses of the NNU 121the words held in the weight RAM 124 are not weights, but arenevertheless referred to as “weight words” because they are stored inthe weight RAM 124. For example, in some uses of the NNU 121, e.g., theconvolution example of FIGS. 24 through 26A or the pooling example ofFIGS. 27 through 28, the weight RAM 124 may hold non-weights, such aselements of a data matrix, e.g., image pixel data. Similarly, althoughin many uses of the NNU 121 the words, or operands, held in the data RAM122 are in fact the output value, or activation, of a neuron, it shouldbe understood that in other uses of the NNU 121 the words held in thedata RAM 122 are not such, but are nevertheless referred to as “datawords” because they are stored in the data RAM 122. For example, in someuses of the NNU 121, e.g., the convolution example of FIGS. 24 through26A, the data RAM 122 may hold non-neuron outputs, such as elements of aconvolution kernel.

In one embodiment, the NPUs 126 and sequencer 128 comprise combinatoriallogic, sequential logic, state machines, or a combination thereof. Anarchitectural instruction (e.g., MFNN instruction 1500) loads thecontents of the status register 127 into one of the GPR 116 to determinethe status of the NNU 121, e.g., that the NNU 121 has completed acommand or completed a program the NNU 121 was running from the programmemory 129, or that the NNU 121 is free to receive a new command orstart a new NNU program.

Advantageously, the number of NPUs 126 may be increased as needed, andthe size of the weight RAM 124 and data RAM 122 may be extended in bothwidth and depth accordingly. Preferably, the weight RAM 124 is largersince in a classic neural network layer there are many connections, andtherefore weights, associated with each neuron. Various embodiments aredescribed herein regarding the size of the data and weight words and thesizes of the weight RAM 124 and data RAM 122 and the number of NPUs 126.In one embodiment, a NNU 121 with a 64 KB (8192 bits×64 rows) data RAM122, a 2 MB (8192 bits×2048 rows) weight RAM 124, and 512 NPUs 126 isimplemented in a Taiwan Semiconductor Manufacturing Company, Limited(TSMC) 16 nm process and occupies approximately a 3.3 mm² area.

The sequencer 128 fetches instructions from the program memory 129 andexecutes them, which includes, among other things, generating addressand control signals for provision to the data RAM 122, weight RAM 124and NPUs 126. The sequencer 128 generates a memory address 123 and aread command for provision to the data RAM 122 to select one of the Drows of N data words for provision to the N NPUs 126. The sequencer 128also generates a memory address 125 and a read command for provision tothe weight RAM 124 to select one of the W rows of N weight words forprovision to the N NPUs 126. The sequence of the addresses 123 and 125generated by the sequencer 128 for provision to the NPUs 126 determinesthe “connections” between neurons. The sequencer 128 also generates amemory address 123 and a write command for provision to the data RAM 122to select one of the D rows of N data words for writing from the N NPUs126. The sequencer 128 also generates a memory address 125 and a writecommand for provision to the weight RAM 124 to select one of the W rowsof N weight words for writing from the N NPUs 126. The sequencer 128also generates a memory address 131 to the program memory 129 to selecta NNU instruction that is provided to the sequencer 128, such asdescribed below. The memory address 131 corresponds to a program counter(not shown) that the sequencer 128 generally increments throughsequential locations of the program memory 129 unless the sequencer 128encounters a control instruction, such as a loop instruction (see, forexample, FIG. 26A), in which case the sequencer 128 updates the programcounter to the target address of the control instruction. The sequencer128 also generates control signals to the NPUs 126 to instruct them toperform various operations or functions, such as initialization,arithmetic/logical operations, rotate and shift operations, activationfunctions and write back operations, examples of which are described inmore detail below (see, for example, micro-operations 3418 of FIG. 34).

The N NPUs 126 generate N result words 133 that may be written back to arow of the weight RAM 124 or to the data RAM 122. Preferably, the weightRAM 124 and the data RAM 122 are directly coupled to the N NPUs 126.More specifically, the weight RAM 124 and data RAM 122 are dedicated tothe NPUs 126 and are not shared by the other execution units 112 of theprocessor 100, and the NPUs 126 are capable of consuming a row from oneor both of the weight RAM 124 and data RAM 122 each clock cycle in asustained manner, preferably in a pipelined fashion. In one embodiment,each of the data RAM 122 and the weight RAM 124 is capable of providing8192 bits to the NPUs 126 each clock cycle. The 8192 bits may beconsumed as 512 16-bit words or as 1024 8-bit words, as described inmore detail below.

Advantageously, the size of the data set that may be processed by theNNU 121 is not limited to the size of the weight RAM 124 and data RAM122, but is rather only limited by the size of system memory since dataand weights may be moved between system memory and the weight RAM 124and data RAM 122 using the MTNN and MFNN instructions (e.g., through themedia registers 118). In one embodiment, the data RAM 122 is dual-portedto enable data words to be written to the data RAM 122 while data wordsare concurrently read from or written to the data RAM 122. Furthermore,the large memory hierarchy of the memory subsystem 114, including thecache memories, provides very high data bandwidth for the transfersbetween the system memory and the NNU 121. Still further, preferably,the memory subsystem 114 includes hardware data prefetchers that trackmemory access patterns, such as loads of neural data and weights fromsystem memory, and perform data prefetches into the cache hierarchy tofacilitate high bandwidth and low latency transfers to the weight RAM124 and data RAM 122.

Although embodiments are described in which one of the operands providedto each NPU 126 is provided from a weight memory and is denoted aweight, which are commonly used in neural networks, it should beunderstood that the operands may be other types of data associated withcalculations whose speed may be improved by the apparatuses described.

Referring now to FIG. 2, a block diagram illustrating a NPU 126 of FIG.1 is shown. The NPU 126 operates to perform many functions, oroperations. In particular, advantageously the NPU 126 is configured tooperate as a neuron, or node, in an artificial neural network to performa classic multiply-accumulate function, or operation. That is, generallyspeaking, the NPU 126 (neuron) is configured to: (1) receive an inputvalue from each neuron having a connection to it, typically but notnecessarily from the immediately previous layer of the artificial neuralnetwork; (2) multiply each input value by a corresponding weight valueassociated with the connection to generate a product; (3) add all theproducts to generate a sum; and (4) perform an activation function onthe sum to generate the output of the neuron. However, rather thanperforming all the multiplies associated with all the connection inputsand then adding all the products together as in a conventional manner,advantageously each neuron is configured to perform, in a given clockcycle, the weight multiply operation associated with one of theconnection inputs and then add (accumulate) the product with theaccumulated value of the products associated with connection inputsprocessed in previous clock cycles up to that point. Assuming there areM connections to the neuron, after all M products have been accumulated(which takes approximately M clock cycles), the neuron performs theactivation function on the accumulated value to generate the output, orresult. This has the advantage of requiring fewer multipliers and asmaller, simpler and faster adder circuit (e.g., a 2-input adder) in theneuron than an adder that would be required to add all, or even a subsetof, the products associated with all the connection inputs. This, inturn, has the advantage of facilitating a very large number (N) ofneurons (NPUs 126) in the NNU 121 so that after approximately M clockcycles, the NNU 121 has generated the output for all of the large number(N) of neurons. Finally, the NNU 121 constructed of such neurons has theadvantage of efficiently performing as an artificial neural networklayer for a large number of different connection inputs. That is, as Mincreases or decreases for different layers, the number of clock cyclesrequired to generate the neuron outputs correspondingly increases ordecreases, and the resources (e.g., multipliers and accumulators) arefully utilized; whereas, in a more conventional design, some of themultipliers and a portion of the adder may not be utilized for smallervalues of M. Thus, the embodiments described herein have the benefit offlexibility and efficiency with respect to the number of connectioninputs to the neurons of the NNU 121, and provide extremely highperformance.

The NPU 126 includes a register 205, a 2-input multiplexed register(mux-reg) 208, an arithmetic logic unit (ALU) 204, an accumulator 202,and an activation function unit (AFU) 212. The register 205 receives aweight word 206 from the weight RAM 124 and provides its output 203 on asubsequent clock cycle. The mux-reg 208 selects one of its inputs 207 or211 to store in its register and then to provide on its output 209 on asubsequent clock cycle. One input 207 receives a data word from the dataRAM 122. The other input 211 receives the output 209 of the adjacent NPU126. The NPU 126 shown in FIG. 2 is denoted NPU J from among the N NPUs126 of FIG. 1. That is, NPU J is a representative instance of the N NPUs126. Preferably, the mux-reg 208 input 211 of NPU J receives the mux-reg208 output 209 of NPU 126 instance J−1, and the mux-reg 208 output 209of NPU J is provided to the mux-reg 208 input 211 of NPU 126 instanceJ+1. In this manner, the mux-regs 208 of the N NPUs 126 collectivelyoperate as an N-word rotater, or circular shifter, as described in moredetail below with respect to FIG. 3. A control input 213 controls whichof the two inputs the mux-reg 208 selects to store in its register andthat is subsequently provided on the output 209.

The ALU 204 has three inputs. One input receives the weight word 203from the register 205. Another input receives the output 209 of themux-reg 208. The other input receives the output 217 of the accumulator202. The ALU 204 performs arithmetic and/or logical operations on itsinputs to generate a result provided on its output. Preferably, thearithmetic and/or logical operations to be performed by the ALU 204 arespecified by instructions stored in the program memory 129. For example,the multiply-accumulate instruction of FIG. 4 specifies amultiply-accumulate operation, i.e., the result 215 is the sum of theaccumulator 202 value 217 and the product of the weight word 203 and thedata word of the mux-reg 208 output 209. Other operations that may bespecified include, but are not limited to: the result 215 is thepassed-through value of the mux-reg output 209; the result 215 is thepassed-through value of the weight word 203; the result 215 is zero; theresult 215 is the passed-through value of the weight word 203; theresult 215 is the sum of the accumulator 202 value 217 and the weightword 203; the result 215 is the sum of the accumulator 202 value 217 andthe mux-reg output 209; the result 215 is the maximum of the accumulator202 value 217 and the weight word 203; the result 215 is the maximum ofthe accumulator 202 value 217 and the mux-reg output 209.

The ALU 204 provides its output 215 to the accumulator 202 for storagetherein. The ALU 204 includes a multiplier 242 that multiplies theweight word 203 and the data word of the mux-reg 208 output 209 togenerate a product 246. In one embodiment, the multiplier 242 multipliestwo 16-bit operands to generate a 32-bit result. The ALU 204 alsoincludes an adder 244 that adds the product 246 to the accumulator 202output 217 to generate a sum, which is the result 215 accumulated in theaccumulator 202 for storage in the accumulator 202. In one embodiment,the adder 244 adds the 32-bit result of the multiplier 242 to a 41-bitvalue 217 of the accumulator 202 to generate a 41-bit result. In thismanner, using the rotater aspect of the mux-reg 208 over the course ofmultiple clock cycles, the NPU 126 accomplishes a sum of products for aneuron as required by neural networks. The ALU 204 may also includeother circuit elements to perform other arithmetic/logical operationssuch as those above. In one embodiment, a second adder subtracts theweight word 203 from the data word of the mux-reg 208 output 209 togenerate a difference, which the adder 244 then adds to the accumulator202 output 217 to generate a sum 215, which is the result accumulated inthe accumulator 202. In this manner, over the course of multiple clockcycles, the NPU 126 may accomplish a sum of differences. Preferably,although the weight word 203 and the data word 209 are the same size (inbits), they may have different binary point locations, as described inmore detail below. Preferably, the multiplier 242 and adder 244 areinteger multipliers and adders, as described in more detail below, toadvantageously accomplish less complex, smaller, faster and lower powerconsuming ALUs 204 than floating-point counterparts. However, it shouldbe understood that in other embodiments the ALU 204 performsfloating-point operations.

Although FIG. 2 shows only a multiplier 242 and adder 244 in the ALU204, preferably the ALU 204 includes other elements to perform the otheroperations described above. For example, preferably the ALU 204 includesa comparator (not shown) for comparing the accumulator 202 with adata/weight word and a mux (not shown) that selects the larger (maximum)of the two values indicated by the comparator for storage in theaccumulator 202. For another example, preferably the ALU 204 includesselection logic (not shown) that bypasses the multiplier 242 with adata/weight word to enable the adder 244 to add the data/weight word tothe accumulator 202 value 217 to generate a sum for storage in theaccumulator 202. These additional operations are described in moredetail below, for example, with respect to FIGS. 18 through 29A, and maybe useful for performing convolution and pooling operations, forexample.

The AFU 212 receives the output 217 of the accumulator 202. The AFU 212performs an activation function on the accumulator 202 output 217 togenerate a result 133 of FIG. 1. Generally speaking, the activationfunction in a neuron of an intermediate layer of an artificial neuralnetwork may serve to normalize the accumulated sum of products,preferably in a non-linear fashion. To “normalize” the accumulated sum,the activation function of an instant neuron produces a resulting valuewithin a range of values that neurons connected to the instant neuronexpect to receive as input. (The normalized result is sometimes referredto as an “activation” that, as described herein, is the output of aninstant node that a receiving node multiplies by a weight associatedwith the connection between the outputting node and the receiving nodeto generate a product that is accumulated with other products associatedwith the other input connections to the receiving node.) For example,the receiving/connected neurons may expect to receive as input a valuebetween 0 and 1, in which case the outputting neuron may need tonon-linearly squash and/or adjust (e.g., upward shift to transformnegative to positive values) the accumulated sum that is outside the 0to 1 range to a value within the expected range. Thus, the AFU 212performs an operation on the accumulator 202 value 217 to bring theresult 133 within a known range. The results 133 of all of the N NPUs126 may be written back concurrently to either the data RAM 122 or tothe weight RAM 124. Preferably, the AFU 212 is configured to performmultiple activation functions, and an input, e.g., from the controlregister 127, selects one of the activation functions to perform on theaccumulator 202 output 217. The activation functions may include, butare not limited to, a step function, a rectify function, a sigmoidfunction, a hyperbolic tangent (tanh) function and a softplus function(also referred to as smooth rectify). The softplus function is theanalytic function f(x)=ln(1+e^(x)), that is, the natural logarithm ofthe sum of one and e^(x), where “e” is Euler's number and x is the input217 to the function. Preferably, the activation functions may alsoinclude a pass-through function that passes through the accumulator 202value 217, or a portion thereof, as described in more detail below. Inone embodiment, circuitry of the AFU 212 performs the activationfunction in a single clock cycle. In one embodiment, the AFU 212comprises tables that receive the accumulated value and output a valuethat closely approximates the value that the true activation functionwould provide for some of the activation functions, e.g., sigmoid,hyperbolic tangent, softplus.

Preferably, the width (in bits) of the accumulator 202 is greater thanthe width of the AFU 212 output 133. For example, in one embodiment, theaccumulator is 41 bits wide, to avoid loss of precision in theaccumulation of up to 512 32-bit products (as described in more detailbelow, e.g., with respect to FIG. 30), and the result 133 is 16 bitswide. In one embodiment, an example of which is described in more detailbelow with respect to FIG. 8, during successive clock cycles differentportions of the “raw” accumulator 202 output 217 value are passedthrough the AFU 212 and written back to the data RAM 122 or weight RAM124. This enables the raw accumulator 202 values to be loaded back tothe media registers 118 via the MFNN instruction so that instructionsexecuting on other execution units 112 of the processor 100 may performcomplex activation functions that the AFU 212 is not capable ofperforming, such as the well-known softmax activation function, alsoreferred to as the normalized exponential function. In one embodiment,the processor 100 instruction set architecture includes an instructionthat performs the exponential function, commonly referred to as e^(x) orexp(x), which may be used to speed up the performance of the softmaxactivation function by the other execution units 112 of the processor100.

In one embodiment, the NPU 126 is pipelined. For example, the NPU 126may include registers of the ALU 204, such as a register between themultiplier and the adder and/or other circuits of the ALU 204, and aregister that holds the output of the AFU 212. Other embodiments of theNPU 126 are described below.

Referring now to FIG. 3, a block diagram illustrating an embodiment ofthe arrangement of the N mux-regs 208 of the N NPUs 126 of the NNU 121of FIG. 1 to illustrate their operation as an N-word rotater, orcircular shifter, for a row of data words 207 received from the data RAM122 of FIG. 1 is shown. In the embodiment of FIG. 3, N is 512 such thatthe NNU 121 has 512 mux-regs 208, denoted 0 through 511, correspondingto 512 NPUs 126, as shown. Each mux-reg 208 receives its correspondingdata word 207 of one row of the D rows of the data RAM 122. That is,mux-reg 0 receives data word 0 of the data RAM 122 row, mux-reg 1receives data word 1 of the data RAM 122 row, mux-reg 2 receives dataword 2 of the data RAM 122 row, and so forth to mux-reg 511 receivesdata word 511 of the data RAM 122 row. Additionally, mux-reg 1 receiveson its other input 211 the output 209 of mux-reg 0, mux-reg 2 receiveson its other input 211 the output 209 of mux-reg 1, mux-reg 3 receiveson its other input 211 the output 209 of mux-reg 2, and so forth tomux-reg 511 that receives on its other input 211 the output 209 ofmux-reg 510, and mux-reg 0 receives on its other input 211 the output209 of mux-reg 511. Each of the mux-regs 208 receives the control input213 that controls whether to select the data word 207 or the rotatedinput 211. As described in more detail below, in one mode of operation,on a first clock cycle, the control input 213 controls each of themux-regs 208 to select the data word 207 for storage in the register andfor subsequent provision to the ALU 204; and during subsequent clockcycles (e.g., M-1 clock cycles as described above), the control input213 controls each of the mux-regs 208 to select the rotated input 211for storage in the register and for subsequent provision to the ALU 204.

Although FIG. 3 (and FIGS. 7 and 19 below) describe an embodiment inwhich the NPUs 126 are configured to rotate the values of the mux-regs208/705 to the right, i.e., from NPU J to NPU J+1, embodiments arecontemplated (such as with respect to the embodiment of FIGS. 24 through26) in which the NPUs 126 are configured to rotate the values of themux-regs 208/705 to the left, i.e., from NPU J to NPU J−1. Furthermore,embodiments are contemplated in which the NPUs 126 are configured torotate the values of the mux-regs 208/705 selectively to the left or tothe right, e.g., as specified by the NNU instructions.

Referring now to FIG. 4, a table illustrating a program for storage inthe program memory 129 of and execution by the NNU 121 of FIG. 1 isshown. The example program performs the calculations associated with alayer of an artificial neural network as described above. In the tableof FIG. 4, four rows and three columns are shown. Each row correspondsto an address of the program memory 129 denoted in the first column. Thesecond column specifies the instruction, and the third column indicatesthe number of clock cycles associated with the instruction. Preferably,the number of clock cycles indicates the effective number of clocks in aclocks-per-instruction type value in a pipelined embodiment, rather thanthe latency of the instruction. As shown, each of the instructions hasan associated one clock cycle due to the pipelined nature of the NNU121, with the exception of the instruction at address 2 which requires511 clocks because it effectively repeats itself 511 times, as describedin more detail below.

For each instruction of the program, all of the NPUs 126 perform theinstruction in parallel. That is, all N NPUs 126 performs theinstruction in the first row in the same clock cycle(s), all N NPUs 126performs the instruction in the second row in the same clock cycle(s),and so forth. However, other embodiments are described below in whichsome of the instructions are performed in a partially parallel andpartially sequential fashion, e.g., the activation function and outputinstructions at addresses 3 and 4 in an embodiment in which NPUs 126share an activation function unit, e.g., with respect to the embodimentof FIG. 11. The example of FIG. 4 assumes 512 neurons (NPUs 126) of alayer, each having 512 connection inputs from a previous layer of 512neurons, for a total of 256K connections. Each neuron receives a 16-bitdata value from each connection input and multiplies the 16-bit datavalue by an appropriate 16-bit weight value.

The first row, at address 0 (although other addresses may be specified),specifies an initialize NPU instruction. The initialize instructionclears the accumulator 202 value to zero. In one embodiment, theinitialize instruction can also specify to load the accumulator 202 withthe corresponding word of a row of the data RAM 122 or weight RAM 124whose address is specified by the instruction. The initializeinstruction also loads configuration values into the control register127, as described in more detail below with respect to FIGS. 29A and29B. For example, the width of the data word 207 and weight word 209 maybe loaded, which may be used by the ALU 204 to determine the sizes ofthe operations performed by the circuits and may affect the result 215stored in the accumulator 202. In one embodiment, the NPU 126 includes acircuit that saturates the ALU 204 output 215 before being stored in theaccumulator 202, and the initialize instruction loads a configurationvalue into the circuit to affect the saturation. In one embodiment, theaccumulator 202 may also be cleared to a zero value by so specifying inan ALU function instruction (e.g., multiply-accumulate instruction ataddress 1) or an output instruction, such as the write AFU outputinstruction at address 4.

The second row, at address 1, specifies a multiply-accumulateinstruction that instructs the 512 NPUs 126 to load a respective dataword from a row of the data RAM 122 and to load a respective weight wordfrom a row of the weight RAM 124, and to perform a firstmultiply-accumulate operation on the data word input 207 and weight wordinput 206, which is accumulated with the initialized accumulator 202zero value. More specifically, the instruction instructs the sequencer128 to generate a value on the control input 213 to select the data wordinput 207. In the example of FIG. 4, the specified data RAM 122 row isrow 17, and the specified weight RAM 124 row is row 0, which instructsthe sequencer 128 to output a data RAM address 123 value of 17 and tooutput a weight RAM address 125 value of 0. Consequently, the 512 datawords from row 17 of the data RAM 122 are provided to the correspondingdata input 207 of the 512 NPUs 126 and the 512 weight words from row 0of the weight RAM 124 are provided to the corresponding weight input 206of the 512 NPUs 126.

The third row, at address 2, specifies a multiply-accumulate rotateinstruction with a count of 511, which instructs each of the 512 NPUs126 to perform 511 multiply-accumulate operations. The instructioninstructs the 512 NPUs 126 that the data word 209 input to the ALU 204for each of the 511 multiply-accumulate operations is to be the rotatedvalue 211 from the adjacent NPU 126. That is, the instruction instructsthe sequencer 128 to generate a value on the control input 213 to selectthe rotated value 211. Additionally, the instruction instructs the 512NPUs 126 to load a respective weight word for each of the 511multiply-accumulate operations from the “next” row of the weight RAM124. That is, the instruction instructs the sequencer 128 to incrementthe weight RAM address 125 by one relative to its value in the previousclock cycle, which in the example would be row 1 on the first clockcycle of the instruction, row 2 on the next clock cycle, row 3 on thenext clock cycle, and so forth to row 511 on the 511^(th) clock cycle.For each of the 511 multiply-accumulate operations, the product of therotated input 211 and weight word input 206 is accumulated with theprevious value in the accumulator 202. The 512 NPUs 126 perform the 511multiply-accumulate operations in 511 clock cycles, in which each NPU126 performs a multiply-accumulate operation on a different data wordfrom row 17 of the data RAM 122—namely, the data word operated on by theadjacent NPU 126 in the previous cycle—and a different weight wordassociated with the data word, which is conceptually a differentconnection input to the neuron. In the example, it is assumed that thenumber of connection inputs to each NPU 126 (neuron) is 512, thusinvolving 512 data words and 512 weight words. Once the last iterationof the multiply-accumulate rotate instruction of row 2 is performed, theaccumulator 202 contains the sum of products for all 512 of theconnection inputs. In one embodiment, rather than having a separateinstruction for each type of ALU operation (e.g., multiply-accumulate,maximum of accumulator and weight word, etc. as described above), theNPU 126 instruction set includes an “execute” instruction that instructsthe ALU 204 to perform an ALU operation specified by the initialize NPUinstruction, such as specified in the ALU function 2926 of FIG. 29A.

The fourth row, at address 3, specifies an activation functioninstruction. The activation function instruction instructs the AFU 212to perform the specified activation function on the accumulator 202value 217 to generate the result 133. The activation functions accordingto one embodiment are described in more detail below.

The fifth row, at address 4, specifies a write AFU output instructionthat instructs the 512 NPUs 126 to write back their AFU 212 output asresults 133 to a row of the data RAM 122, which is row 16 in theexample. That is, the instruction instructs the sequencer 128 to outputa data RAM address 123 value of 16 and a write command (in contrast to aread command in the case of the multiply-accumulate instruction ataddress 1). Preferably the execution of the write AFU output instructionmay be overlapped with the execution of other instructions in apipelined nature such that the write AFU output instruction effectivelyexecutes in a single clock cycle.

Preferably, each NPU 126 is configured as a pipeline that includes thevarious functional elements, e.g., the mux-reg 208 (and mux-reg 705 ofFIG. 7), ALU 204, accumulator 202, AFU 212, mux 802 (of FIG. 8), rowbuffer 1104 and AFUs 1112 (of FIG. 11), etc., some of which maythemselves be pipelined. In addition to the data words 207 and weightwords 206, the pipeline receives the instructions from the programmemory 129. The instructions flow down the pipeline and control thevarious functional units. In an alternate embodiment, the activationfunction instruction is not included in the program. Rather, theinitialize NPU instruction specifies the activation function to beperformed on the accumulator 202 value 217, and a value indicating thespecified activation function is saved in a configuration register forlater use by the AFU 212 portion of the pipeline once the finalaccumulator 202 value 217 has been generated, i.e., once the lastiteration of the multiply-accumulate rotate instruction at address 2 hascompleted. Preferably, for power savings purposes, the AFU 212 portionof the pipeline is inactive until the write AFU output instructionreaches it, at which time the AFU 212 is powered up and performs theactivation function on the accumulator 202 output 217 specified by theinitialize instruction.

Referring now to FIG. 5, a timing diagram illustrating the execution ofthe program of FIG. 4 by the NNU 121 is shown. Each row of the timingdiagram corresponds to a successive clock cycle indicated in the firstcolumn. Each of the other columns corresponds to a different one of the512 NPUs 126 and indicates its operation. For simplicity and clarity ofillustration, the operations only for NPUs 0, 1 and 511 are shown.

At clock 0, each of the 512 NPUs 126 performs the initializationinstruction of FIG. 4, which is illustrated in FIG. 5 by the assignmentof a zero value to the accumulator 202.

At clock 1, each of the 512 NPUs 126 performs the multiply-accumulateinstruction at address 1 of FIG. 4. NPU 0 accumulates the accumulator202 value (which is zero) with the product of data RAM 122 row 17 word 0and weight RAM 124 row 0 word 0; NPU 1 accumulates the accumulator 202value (which is zero) with the product of data RAM 122 row 17 word 1 andweight RAM 124 row 0 word 1; and so forth to NPU 511 accumulates theaccumulator 202 value (which is zero) with the product of data RAM 122row 17 word 511 and weight RAM 124 row 0 word 511, as shown.

At clock 2, each of the 512 NPUs 126 performs a first iteration of themultiply-accumulate rotate instruction at address 2 of FIG. 4. NPU 0accumulates the accumulator 202 value with the product of the rotateddata word 211 received from the mux-reg 208 output 209 of NPU 511 (whichwas data word 511 received from the data RAM 122) and weight RAM 124 row1 word 0; NPU 1 accumulates the accumulator 202 value with the productof the rotated data word 211 received from the mux-reg 208 output 209 ofNPU 0 (which was data word 0 received from the data RAM 122) and weightRAM 124 row 1 word 1; and so forth to NPU 511 accumulates theaccumulator 202 value with the product of the rotated data word 211received from the mux-reg 208 output 209 of NPU 510 (which was data word510 received from the data RAM 122) and weight RAM 124 row 1 word 511,as shown.

At clock 3, each of the 512 NPUs 126 performs a second iteration of themultiply-accumulate rotate instruction at address 2 of FIG. 4. NPU 0accumulates the accumulator 202 value with the product of the rotateddata word 211 received from the mux-reg 208 output 209 of NPU 511 (whichwas data word 510 received from the data RAM 122) and weight RAM 124 row2 word 0; NPU 1 accumulates the accumulator 202 value with the productof the rotated data word 211 received from the mux-reg 208 output 209 ofNPU 0 (which was data word 511 received from the data RAM 122) andweight RAM 124 row 2 word 1; and so forth to NPU 511 accumulates theaccumulator 202 value with the product of the rotated data word 211received from the mux-reg 208 output 209 of NPU 510 (which was data word509 received from the data RAM 122) and weight RAM 124 row 2 word 511,as shown. As indicated by the ellipsis of FIG. 5, this continues foreach of the following 509 clock cycles until . . .

At clock 512, each of the 512 NPUs 126 performs a 511^(th) iteration ofthe multiply-accumulate rotate instruction at address 2 of FIG. 4. NPU 0accumulates the accumulator 202 value with the product of the rotateddata word 211 received from the mux-reg 208 output 209 of NPU 511 (whichwas data word 1 received from the data RAM 122) and weight RAM 124 row511 word 0; NPU 1 accumulates the accumulator 202 value with the productof the rotated data word 211 received from the mux-reg 208 output 209 ofNPU 0 (which was data word 2 received from the data RAM 122) and weightRAM 124 row 511 word 1; and so forth to NPU 511 accumulates theaccumulator 202 value with the product of the rotated data word 211received from the mux-reg 208 output 209 of NPU 510 (which was data word0 received from the data RAM 122) and weight RAM 124 row 511 word 511,as shown. In one embodiment, multiple clock cycles are required to readthe data words and weight words from the data RAM 122 and weight RAM 124to perform the multiply-accumulate instruction at address 1 of FIG. 4;however, the data RAM 122 and weight RAM 124 and NPUs 126 are pipelinedsuch that once the first multiply-accumulate operation is begun (e.g.,as shown during clock 1 of FIG. 5), the subsequent multiply accumulateoperations (e.g., as shown during clocks 2-512) are begun in successiveclock cycles. Preferably, the NPUs 126 may briefly stall in response toan access of the data RAM 122 and/or weight RAM 124 by an architecturalinstruction, e.g., MTNN or MFNN instruction (described below withrespect to FIGS. 14 and 15) or a microinstruction into which thearchitectural instructions are translated.

At clock 513, the AFU 212 of each of the 512 NPUs 126 performs theactivation function instruction at address 3 of FIG. 4. Finally, atclock 514, each of the 512 NPUs 126 performs the write AFU outputinstruction at address 4 of FIG. 4 by writing back its result 133 to itscorresponding word of row 16 of the data RAM 122, i.e., the result 133of NPU 0 is written to word 0 of the data RAM 122, the result 133 of NPU1 is written to word 1 of the data RAM 122, and so forth to the result133 of NPU 511 is written to word 511 of the data RAM 122. The operationdescribed above with respect to FIG. 5 is also shown in block diagramform in FIG. 6A.

Referring now to FIG. 6A, a block diagram illustrating the NNU 121 ofFIG. 1 to execute the program of FIG. 4 is shown. The NNU 121 includesthe 512 NPUs 126, the data RAM 122 that receives its address input 123,and the weight RAM 124 that receives its address input 125. Although notshown, on clock 0 the 512 NPUs 126 perform the initializationinstruction. As shown, on clock 1, the 512 16-bit data words of row 17are read out of the data RAM 122 and provided to the 512 NPUs 126. Onclocks 1 through 512, the 512 16-bit weight words of rows 0 through 511,respectively, are read out of the weight RAM 124 and provided to the 512NPUs 126. Although not shown, on clock 1, the 512 NPUs 126 perform theirrespective multiply-accumulate operations on the loaded data words andweight words. On clocks 2 through 512, the mux-regs 208 of the 512 NPUs126 operate as a 512 16-bit word rotater to rotate the previously loadeddata words of row 17 of the data RAM 122 to the adjacent NPU 126, andthe NPUs 126 perform the multiply-accumulate operation on the respectiverotated data word and the respective weight word loaded from the weightRAM 124. Although not shown, on clock 513, the 512 AFUs 212 perform theactivation instruction. On clock 514, the 512 NPUs 126 write back theirrespective 512 16-bit results 133 to row 16 of the data RAM 122.

As may be observed, the number clocks required to generate the resultwords (neuron outputs) produced and written back to the data RAM 122 orweight RAM 124 is approximately the square root of the number of datainputs (connections) received by the current layer of the neuralnetwork. For example, if the currently layer has 512 neurons that eachhas 512 connections from the previous layer, the total number ofconnections is 256K and the number of clocks required to generate theresults for the current layer is slightly over 512. Thus, the NNU 121provides extremely high performance for neural network computations.

Referring now to FIG. 6B, a flowchart illustrating operation of theprocessor 100 of FIG. 1 to perform an architectural program that usesthe NNU 121 to perform multiply-accumulate-activation functioncomputations classically associated with neurons of hidden layers of anartificial neural network such as performed by the program of FIG. 4,for example. The example of FIG. 6B assumes computations for 4 hiddenlayers (signified by the initialization of the NUM_LAYERS variable atblock 602), each having 512 neurons each fully connected to 512 neuronsof the previous layer (by use of the program of FIG. 4). However, itshould be understood that these numbers of layers and neurons areselected for illustration purposes, and the NNU 121 may be employed toperform similar computations for different numbers of hidden layers anddifferent numbers of neurons per layer and for non-fully connectedneurons. In one embodiment, the weight values may be set to zero fornon-existent neurons in a layer or for non-existent connections to aneuron. Preferably, the architectural program writes a first set ofweights to the weight RAM 124 and starts the NNU 121, and while the NNU121 is performing the computations associated with the first layer, thearchitectural program writes a second set of weights to the weight RAM124 so that as soon as the NNU 121 completes the computations for thefirst hidden layer, the NNU 121 can start the computations for thesecond layer. In this manner, the architectural program ping-pongs backand forth between the two regions of the weight RAM 124 in order to keepthe NNU 121 fully utilized. Flow begins at block 602.

At block 602, the processor 100, i.e., the architectural program runningon the processor 100, writes the input values to the current hiddenlayer of neurons to the data RAM 122, e.g., into row 17 of the data RAM122, as shown and described with respect to FIG. 6A. Alternatively, thevalues may already be in row 17 of the data RAM 122 as results 133 ofthe operation of the NNU 121 for a previous layer (e.g., convolution,pooling or input layer). Additionally, the architectural programinitializes a variable N to a value of 1. The variable N denotes thecurrent layer of the hidden layers being processed by the NNU 121.Additionally, the architectural program initializes a variable NUMLAYERS to a value of 4 since there are 4 hidden layers in the example.Flow proceeds to block 604.

At block 604, the processor 100 writes the weight words for layer 1 tothe weight RAM 124, e.g., to rows 0 through 511, as shown in FIG. 6A.Flow proceeds to block 606.

At block 606, the processor 100 writes a multiply-accumulate-activationfunction program (e.g., of FIG. 4) to the NNU 121 program memory 129,using MTNN 1400 instructions that specify a function 1432 to write theprogram memory 129. The processor 100 then starts the NNU program usinga MTNN 1400 instruction that specifies a function 1432 to startexecution of the program. Flow proceeds to decision block 608.

At decision block 608, the architectural program determines whether thevalue of variable N is less than NUM LAYERS. If so, flow proceeds toblock 612; otherwise, flow proceeds to block 614.

At block 612, the processor 100 writes the weight words for layer N+1 tothe weight RAM 124, e.g., to rows 512 through 1023. Thus,advantageously, the architectural program writes the weight words forthe next layer to the weight RAM 124 while the NNU 121 is performing thehidden layer computations for the current layer so that the NNU 121 canimmediately start performing the hidden layer computations for the nextlayer once the computations for the current layer are complete, i.e.,written to the data RAM 122. Flow proceeds to block 614.

At block 614, the processor 100 determines that the currently runningNNU program (started at block 606 in the case of layer 1, and started atblock 618 in the case of layers 2 through 4) has completed. Preferably,the processor 100 determines this by executing a MFNN 1500 instructionto read the NNU 121 status register 127. In an alternate embodiment, theNNU 121 generates an interrupt to indicate it has completed themultiply-accumulate-activation function layer program. Flow proceeds todecision block 616.

At decision block 616, the architectural program determines whether thevalue of variable N is less than NUM LAYERS. If so, flow proceeds toblock 618; otherwise, flow proceeds to block 622.

At block 618, the processor 100 updates themultiply-accumulate-activation function program so that it can performthe hidden layer computations for layer N+1. More specifically, theprocessor 100 updates the data RAM 122 row value of themultiply-accumulate instruction at address 1 of FIG. 4 to the row of thedata RAM 122 to which the previous layer wrote its results (e.g., to row16) and also updates the output row (e.g., to row 15). The processor 100then starts the updated NNU program. Alternatively, the program of FIG.4 specifies the same row in the output instruction of address 4 as therow specified in the multiply-accumulate instruction at address 1 (i.e.,the row read from the data RAM 122). In this embodiment, the current rowof input data words is overwritten (which is acceptable as long as therow of data words is not needed for some other purpose, because the rowof data words has already been read into the mux-regs 208 and is beingrotated among the NPUs 126 via the N-word rotater). In this case, noupdate of the NNU program is needed at block 618, but only a re-start ofit. Flow proceeds to block 622.

At block 622, the processor 100 reads the results of the NNU programfrom the data RAM 122 for layer N. However, if the results are simply tobe used by the next layer, then the architectural program may not needto read the results from the data RAM 122, but instead leave them in thedata RAM 122 for the next hidden layer computations. Flow proceeds todecision block 624.

At decision block 624, the architectural program determines whether thevalue of variable N is less than NUM LAYERS. If so, flow proceeds toblock 626; otherwise, flow ends.

At block 626, the architectural program increments N by one. Flowreturns to decision block 608.

As may be determined from the example of FIG. 6B, approximately every512 clock cycles, the NPUs 126 read once from and write once to the dataRAM 122 (by virtue of the operation of the NNU program of FIG. 4).Additionally, the NPUs 126 read the weight RAM 124 approximately everyclock cycle to read a row of the weight words. Thus, the entirebandwidth of the weight RAM 124 is consumed by the hybrid manner inwhich the NNU 121 performs the hidden layer operation. Additionally,assuming an embodiment that includes a write and read buffer such as thebuffer 1704 of FIG. 17, concurrently with the NPU 126 reads, theprocessor 100 writes the weight RAM 124 such that the buffer 1704performs one write to the weight RAM 124 approximately every 16 clockcycles to write the weight words. Thus, in a single-ported embodiment ofthe weight RAM 124 (such as described with respect to FIG. 17),approximately every 16 clock cycles, the NPUs 126 must be stalled fromreading the weight RAM 124 to enable the buffer 1704 to write the weightRAM 124. However, in an embodiment in which the weight RAM 124 isdual-ported, the NPUs 126 need not be stalled.

Referring now to FIG. 7, a block diagram illustrating a NPU 126 of FIG.1 according to an alternate embodiment is shown. The NPU 126 of FIG. 7is similar in many respects to the NPU 126 of FIG. 2. However, the NPU126 of FIG. 7 additionally includes a second 2-input mux-reg 705. Themux-reg 705 selects one of its inputs 206 or 711 to store in itsregister and then to provide on its output 203 on a subsequent clockcycle. Input 206 receives the weight word from the weight RAM 124. Theother input 711 receives the output 203 of the second mux-reg 705 of theadjacent NPU 126. Preferably, the mux-reg 705 input 711 of NPU Jreceives the mux-reg 705 output 203 of NPU 126 instance J−1, and theoutput of NPU J is provided to the mux-reg 705 input 711 of NPU 126instance J+1. In this manner, the mux-regs 705 of the N NPUs 126collectively operate as an N-word rotater, similar to the mannerdescribed above with respect to FIG. 3, but for the weight words ratherthan for the data words. A control input 713 controls which of the twoinputs the mux-reg 705 selects to store in its register and that issubsequently provided on the output 203.

Including the mux-regs 208 and/or mux-regs 705 (as well as the mux-regsof other embodiments, such as of FIGS. 18 and 23) to effectively form alarge rotater that rotates the data/weights of a row received from thedata RAM 122 and/or weight RAM 124 has an advantage that the NNU 121does not require an extremely large mux that would otherwise be requiredbetween the data RAM 122 and/or weight RAM 124 in order to provide thenecessary data/weight words to the appropriate NNU 121.

Writing Back Accumulator Values in Addition to Activation FunctionResult

In some applications, it is useful for the processor 100 to receive back(e.g., to the media registers 118 via the MFNN instruction of FIG. 15)the raw accumulator 202 value 217 upon which instructions executing onother execution units 112 can perform computations. For example, in oneembodiment, in order to reduce the complexity of the AFU 212, it is notconfigured to perform the softmax activation function. Consequently, theNNU 121 may output the raw accumulator 202 value 217, or a subsetthereof, to the data RAM 122 or weight RAM 124, which the architecturalprogram subsequently reads from the data RAM 122 or weight RAM 124 andperforms computations on the raw values. However, use of the rawaccumulator 202 value 217 is not limited to performance of softmax, andother uses are contemplated.

Referring now to FIG. 8, a block diagram illustrating a NPU 126 of FIG.1 according to an alternate embodiment is shown. The NPU 126 of FIG. 8is similar in many respects to the NPU 126 of FIG. 2. However, the NPU126 of FIG. 8 includes a multiplexer (mux) 802 in the AFU 212 that has acontrol input 803. The width (in bits) of the accumulator 202 is greaterthan the width of a data word. The mux 802 has multiple inputs thatreceive data word-width portions of the accumulator 202 output 217. Inone embodiment, the width of the accumulator 202 is 41 bits and the NPU126 is configured to output a result word 133 that is 16 bits; thus, forexample, the mux 802 (or mux 3032 and/or mux 3037 of FIG. 30) includesthree inputs that receive bits [15:0], bits [31:16], and bits [47:32] ofthe accumulator 202 output 217, respectively. Preferably, output bitsnot provided by the accumulator 202 (e.g., bits [47:41]) are forced tozero value bits.

The sequencer 128 generates a value on the control input 803 to controlthe mux 802 to select one of the words (e.g., 16 bits) of theaccumulator 202 in response to a write ACC instruction such as the writeACC instructions at addresses 3 through 5 of FIG. 9 described below.Preferably, the mux 802 also has one or more inputs that receive theoutput of activation function circuits (e.g., elements 3022, 3024, 3026,3018, 3014, and 3016 of FIG. 30) that generate outputs that are thewidth of a data word. The sequencer 128 generates a value on the controlinput 803 to control the mux 802 to select one of the activationfunction circuit outputs, rather than one of the words of theaccumulator 202, in response to an instruction such as the write AFUoutput instruction at address 4 of FIG. 4.

Referring now to FIG. 9, a table illustrating a program for storage inthe program memory 129 of and execution by the NNU 121 of FIG. 1 isshown. The example program of FIG. 9 is similar in many respects to theprogram of FIG. 4. Specifically, the instructions at addresses 0 through2 are identical. However, the instructions at addresses 3 and 4 of FIG.4 are replaced in FIG. 9 by write ACC instructions that instruct the 512NPUs 126 to write back their accumulator 202 output 217 as results 133to three rows of the data RAM 122, which is rows 16 through 18 in theexample. That is, the write ACC instruction instructs the sequencer 128to output a data RAM address 123 value of 16 and a write command in afirst clock cycle, to output a data RAM address 123 value of 17 and awrite command in a second clock cycle, and to output a data RAM address123 value of 18 and a write command in a third clock cycle. Preferablythe execution of the write ACC instruction may be overlapped with theexecution of other instructions such that the write ACC instructioneffectively executes in three clock cycles, one for each row written toin the data RAM 122. In one embodiment, the user specifies values of theactivation function 2934 and output command 2956 fields in the controlregister 127 (of FIG. 29A) to accomplish the writing of the desiredportions of the accumulator 202 to the data RAM 122 or weight RAM 124.Alternatively, rather than writing back the entire contents of theaccumulator 202, the write ACC instruction may optionally write back asubset of the accumulator 202. In one embodiment, a canonical form ofthe accumulator 202 may written back, as described in more detail belowwith respect to FIGS. 29 through 31.

Referring now to FIG. 10, a timing diagram illustrating the execution ofthe program of FIG. 9 by the NNU 121 is shown. The timing diagram ofFIG. 10 is similar to the timing diagram of FIG. 5, and clocks 0 through512 are the same. However, at clocks 513-515, the AFU 212 of each of the512 NPUs 126 performs one of the write ACC instructions at addresses 3through 5 of FIG. 9. Specifically, at clock 513, each of the 512 NPUs126 writes back as its result 133 to its corresponding word of row 16 ofthe data RAM 122 bits [15:0] of the accumulator 202 output 217; at clock514, each of the 512 NPUs 126 writes back as its result 133 to itscorresponding word of row 17 of the data RAM 122 bits [31:16] of theaccumulator 202 output 217; and at clock 515, each of the 512 NPUs 126writes back as its result 133 to its corresponding word of row 18 of thedata RAM 122 bits [40:32] of the accumulator 202 output 217. Preferably,bits [47:41] are forced to zero values.

Shared AFUs

Referring now to FIG. 11, a block diagram illustrating an embodiment ofthe NNU 121 of FIG. 1 is shown. In the embodiment of FIG. 11, a neuronis split into two portions, the activation function unit portion and theALU portion (which also includes the shift register portion), and eachactivation function unit portion is shared by multiple ALU portions. InFIG. 11, the ALU portions are referred to as NPUs 126 and the sharedactivation function unit portions are referred to as AFUs 1112. This isin contrast to the embodiment of FIG. 2, for example, in which eachneuron includes its own AFU 212. Hence, for example, in one embodimentthe NPUs 126 (ALU portions) of the embodiment of FIG. 11 include theaccumulator 202, ALU 204, mux-reg 208 and register 205 of FIG. 2, butnot the AFU 212. In the embodiment of FIG. 11, the NNU 121 includes 512NPUs 126 as an example; however, other embodiments with other numbers ofNPUs 126 are contemplated. In the example of FIG. 11, the 512 NPUs 126are grouped into 64 groups of eight NPUs 126 each, referred to as groups0 through 63 in FIG. 11.

The NNU 121 also includes a row buffer 1104 and a plurality of sharedAFUs 1112 coupled between the NPUs 126 and the row buffer 1104. The rowbuffer 1104 is the same width (in bits) as a row of the data RAM 122 orweight RAM 124, e.g., 512 words. There is one AFU 1112 per NPU 126group, i.e., each AFU 1112 has a corresponding NPU 126 group; thus, inthe embodiment of FIG. 11 there are 64 AFUs 1112 that correspond to the64 NPU 126 groups. Each of the eight NPUs 126 in a group shares thecorresponding AFU 1112. Other embodiments with different numbers of AFUs1112 and NPUs 126 per group are contemplated. For example, otherembodiments are contemplated in which two or four or sixteen NPUs 126 ina group share an AFU 1112.

A motivation for sharing AFUs 1112 is to reduce the size of the NNU 121.The size reduction is obtained at the cost of a performance reduction.That is, it may take several clocks longer, depending upon the sharingratio, to generate the results 133 for the entire array of NPUs 126, asdemonstrated in FIG. 12 below, for example, in which seven additionalclock cycles are required because of the 8:1 sharing ratio. However,generally speaking, the additional number of clocks (e.g., 7) isrelatively small compared to the number of clocks required to generatedthe accumulated sum (e.g., 512 clocks for a layer that has 512connections per neuron). Hence, the relatively small performance impact(e.g., one percent increase in computation time) may be a worthwhiletradeoff for the reduced size of the NNU 121.

In one embodiment, each of the NPUs 126 includes an AFU 212 thatperforms relatively simple activation functions, thus enabling thesimple AFUs 212 to be relatively small and therefore included in eachNPU 126; whereas, the shared, or complex, AFUs 1112 perform relativelycomplex activation functions and are thus relatively significantlylarger than the simple AFUs 212. In such an embodiment, the additionalclock cycles are only required when a complex activation function isspecified that requires sharing of a complex AFU 1112, but not when anactivation function is specified that the simple AFU 212 is configuredto perform.

Referring now to FIGS. 12 and 13, two timing diagrams illustrating theexecution of the program of FIG. 4 by the NNU 121 of FIG. 11 is shown.The timing diagram of FIG. 12 is similar to the timing diagram of FIG.5, and clocks 0 through 512 are the same. However, at clock 513,operation is different than described in the timing diagram of FIG. 5because the NPUs 126 of FIG. 11 share the AFUs 1112; that is, the NPUs126 of a group share the AFU 1112 associated with the group, and FIG. 11illustrates the sharing.

Each row of the timing diagram of FIG. 13 corresponds to a successiveclock cycle indicated in the first column. Each of the other columnscorresponds to a different one of the 64 AFUs 1112 and indicates itsoperation. For simplicity and clarity of illustration, the operationsonly for AFUs 0, 1 and 63 are shown. The clock cycles of FIG. 13correspond to the clock cycles of FIG. 12 but illustrate the sharing ofthe AFUs 1112 by the NPUs 126 in a different manner. At clocks 0-512,each of the 64 AFUs 1112 is inactive, as shown in FIG. 13, while theNPUs 126 perform the initialize NPU and multiply-accumulate andmultiply-accumulate rotate instructions.

As shown in both FIGS. 12 and 13, at clock 513, AFU 0 (the AFU 1112associated with group 0) begins to perform the specified activationfunction on the accumulator 202 value 217 of NPU 0, which is the firstNPU 126 in group 0, and the output of AFU 0 will be stored to row buffer1104 word 0. Also at clock 513, each of the AFUs 1112 begins to performthe specified activation function on the accumulator 202 of the firstNPU 126 in its corresponding group of NPUs 126. Thus, in clock 513, asshown in FIG. 13, AFU 0 begins to perform the specified activationfunction on the accumulator 202 of NPU 0 to generate a result that willbe stored to row buffer 1104 word 0; AFU 1 begins to perform thespecified activation function on the accumulator 202 of NPU 8 togenerate a result that will be stored to row buffer 1104 word 8; and soforth to AFU 63 begins to perform the specified activation function onthe accumulator 202 of NPU 504 to generate a result that will be storedto row buffer 1104 word 504.

At clock 514, AFU 0 (the AFU 1112 associated with group 0) begins toperform the specified activation function on the accumulator 202 value217 of NPU 1, which is the second NPU 126 in group 0, and the output ofAFU 0 will be stored to row buffer 1104 word 1, as shown. Also at clock514, each of the AFUs 1112 begins to perform the specified activationfunction on the accumulator 202 of the second NPU 126 in itscorresponding group of NPUs 126. Thus, in clock 514, as shown in FIG.13, AFU 0 begins to perform the specified activation function on theaccumulator 202 of NPU 1 to generate a result that will be stored to rowbuffer 1104 word 1; AFU 1 begins to perform the specified activationfunction on the accumulator 202 of NPU 9 to generate a result that willbe stored to row buffer 1104 word 9; and so forth to AFU 63 begins toperform the specified activation function on the accumulator 202 of NPU505 to generate a result that will be stored to row buffer 1104 word505. This pattern continues until at clock cycle 520, AFU 0 (the AFU1112 associated with group 0) begins to perform the specified activationfunction on the accumulator 202 value 217 of NPU 7, which is the eighth(last) NPU 126 in group 0, and the output of AFU 0 will be stored to rowbuffer 1104 word 7, as shown. Also at clock 520, each of the AFUs 1112begins to perform the specified activation function on the accumulator202 of the eighth NPU 126 in its corresponding group of NPUs 126. Thus,in clock 520, as shown in FIG. 13, AFU 0 begins to perform the specifiedactivation function on the accumulator 202 of NPU 7 to generate a resultthat will be stored to row buffer 1104 word 7; AFU 1 begins to performthe specified activation function on the accumulator 202 of NPU 15 togenerate a result that will be stored to row buffer 1104 word 15; and soforth to AFU 63 begins to perform the specified activation function onthe accumulator 202 of NPU 511 to generate a result that will be storedto row buffer 1104 word 511.

At clock 521, once all 512 results associated with the 512 NPUs 126 havebeen generated and written to the row buffer 1104, the row buffer 1104begins to write its contents to the data RAM 122 or weight RAM 124. Inthis fashion, the AFU 1112 of each of the 64 groups of NPUs 126 performsa portion of the activation function instruction at address 3 of FIG. 4.

Embodiments such as that of FIG. 11 that share AFUs 1112 among groups ofALUs 204 may be particularly advantageous in conjunction with integerALUs 204, as described more below, e.g., with respect to FIGS. 29Athrough 33.

MTNN and MFNN Architectural Instructions

Referring now to FIG. 14, a block diagram illustrating a move to neuralnetwork (MTNN) architectural instruction 1400 and its operation withrespect to portions of the NNU 121 of FIG. 1 is shown. The MTNNinstruction 1400 includes an opcode field 1402, a src1 field 1404, asrc2 field 1406, a gpr field 1408, and an immediate field 1412. The MTNNinstruction 1400 is an architectural instruction, i.e., it is includedin the instruction set architecture of the processor 100. Preferably,the instruction set architecture associates a predetermined value of theopcode field 1402 with the MTNN instruction 1400 to distinguish it fromother instructions in the instruction set architecture. The MTNNinstruction 1400 opcode 1402 may or may not include prefixes, such asare common, for example, in the x86 architecture.

The immediate field 1412 provides a value that specifies a function 1432to control logic 1434 of the NNU 121. Preferably, the function 1432 isprovided as an immediate operand of a microinstruction 105 of FIG. 1.The functions 1432 that may be performed by the NNU 121 include, but arenot limited to, writing to the data RAM 122, writing to the weight RAM124, writing to the program memory 129, writing to the control register127, starting execution of a program in the program memory 129, pausingthe execution of a program in the program memory 129, requestnotification (e.g., interrupt) of completion of the execution of aprogram in the program memory 129, and resetting the NNU 121.Preferably, the NNU instruction set includes an instruction whose resultindicates the NNU program is complete. Alternatively, the

NNU instruction set includes an explicit generate interrupt instruction.Preferably, resetting the NNU 121 includes effectively forcing the NNU121 back to a reset state (e.g., internal state machines are cleared andset to an idle state), except the contents of the data RAM 122, weightRAM 124, program memory 129 are left intact. Additionally, internalregisters such as the accumulator 202 are not affected by the resetfunction and must be explicitly cleared, e.g., by an initialize NPUinstruction at address 0 of FIG. 4. In one embodiment, the function 1432may include a direct execution function in which the first sourceregister contains a micro-operation (see for example micro-operation3418 of FIG. 34). The direct execution function instructs the NNU 121 todirectly execute the specified micro-operation. In this manner, anarchitectural program may directly control the NNU 121 to performoperations, rather than writing instructions to the program memory 129and then instructing the NNU 121 to execute the instructions in theprogram memory or by executing an MTNN instruction 1400 (or an MFNNinstruction 1500 of FIG. 15). FIG. 14 illustrates an example of thefunction 1432 of writing to the data RAM 122.

The gpr field 1408 specifies one of the GPR in the general purposeregister file 116. In one embodiment, each GPR is 64 bits. The generalpurpose register file 116 provides the value from the selected GPR tothe NNU 121, as shown, which uses the value as an address 1422. Theaddress 1422 selects a row of the memory specified in the function 1432.In the case of the data RAM 122 or weight RAM 124, the address 1422additionally selects a chunk that is twice the size of a media register(e.g., 512 bits) location within the selected row. Preferably, thelocation is on a 512-bit boundary. In one embodiment, a multiplexerselects either the address 1422 (or address 1422 in the case of a MFNNinstruction 1400 described below) or the address 123/125/131 from thesequencer 128 for provision to the data RAM 122/weight RAM 124/programmemory 129. In one embodiment, as described in more detail below, thedata RAM 122 is dual-ported to allow the NPUs 126 to read/write the dataRAM 122 concurrently with the media registers 118 reading/writing thedata RAM 122. In one embodiment, the weight RAM 124 is also dual-portedfor a similar purpose.

The src1 field 1404 and src2 field 1406 each specify a media register inthe media register file 118. In one embodiment, each media register 118is 256 bits. The media register file 118 provides the concatenated data(e.g., 512 bits) from the selected media registers to the data RAM 122(or weight RAM 124 or program memory 129) for writing into the selectedrow 1428 specified by the address 1422 and into the location specifiedby the address 1422 within the selected row 1428, as shown.Advantageously, by executing a series of MTNN instructions 1400 (andMFNN instructions 1400 described below), an architectural programexecuting on the processor 100 can populate rows of the data RAM 122 androws of the weight RAM 124 and write a program to the program memory129, such as the programs described herein (e.g., of FIGS. 4 and 9) tocause the NNU 121 to perform operations on the data and weights atextremely high speeds to accomplish an artificial neural network. In oneembodiment, the architectural program directly controls the NNU 121rather than writing a program into the program memory 129.

In one embodiment, rather than specifying two source registers (e.g.,1404 and 1406), the MTNN instruction 1400 specifies a start sourceregister and a number of source registers, Q. This form of the MTNNinstruction 1400 instructs the processor 100 to write the media register118 specified as the start source register as well as the next Q−1sequential media registers 118 to the NNU 121, i.e., to the data RAM 122or weight RAM 124 specified. Preferably, the instruction translator 104translates the MTNN instruction 1400 into as many microinstructions asneeded to write all the Q specified media registers 118. For example, inone embodiment, when the MTNN instruction 1400 specifies a start sourceregister as MR4 and Q is 8, then the instruction translator 104translates the MTNN instruction 1400 into four microinstructions, thefirst of which writes MR4 and MR5, the second of which writes MR6 andMR7, the third of which writes MR8 and MR9, and the fourth of whichwrites MR10 and MR11. In an alternate embodiment in which the data pathfrom the media registers 118 to the NNU 121 is 1024 bits rather than512, the instruction translator 104 translates the MTNN instruction 1400into two microinstructions, the first of which writes MR4 through MR7,and the second of which writes MR8 through MR11. A similar embodiment iscontemplated in which the MFNN instruction 1500 specifies a startdestination register and a number of destination registers, to enablereading larger chunks of a row of the data RAM 122 or weight RAM 124 perMFNN instruction 1500 than a single media register 118.

Referring now to FIG. 15, a block diagram illustrating a move fromneural network (MFNN) architectural instruction 1500 and its operationwith respect to portions of the NNU 121 of FIG. 1 is shown. The MFNNinstruction 1500 includes an opcode field 1502, a dst field 1504, a gprfield 1508, and an immediate field 1512. The MFNN instruction 1500 is anarchitectural instruction, i.e., it is included in the instruction setarchitecture of the processor 100. Preferably, the instruction setarchitecture associates a predetermined value of the opcode field 1502with the MFNN instruction 1500 to distinguish it from other instructionsin the instruction set architecture. The MFNN instruction 1500 opcode1502 may or may not include prefixes, such as are common, for example,in the x86 architecture.

The immediate field 1512 provides a value that specifies a function 1532to the control logic 1434 of the NNU 121. Preferably, the function 1532is provided as an immediate operand of a microinstruction 105 of FIG. 1.The functions 1532 that may be performed by the NNU 121 include, but arenot limited to, reading from the data RAM 122, reading from the weightRAM 124, reading from the program memory 129, and reading from thestatus register 127. FIG. 15 illustrates an example of the function 1532of reading from the data RAM 122.

The gpr field 1508 specifies one of the GPR in the general purposeregister file 116. The general purpose register file 116 provides thevalue from the selected GPR to the NNU 121, as shown, which uses thevalue as an address 1522 that operates in a manner similar to theaddress 1422 of FIG. 14 to select a row of the memory specified in thefunction 1532 and, in the case of the data RAM 122 or weight RAM 124,the address 1522 additionally selects a chunk that is the size of amedia register (e.g., 256 bits) location within the selected row.Preferably, the location is on a 256-bit boundary.

The dst field 1504 specifies a media register in the media register file118. The media register file 118 receives the data (e.g., 256 bits) intothe selected media register from the data RAM 122 (or weight RAM 124 orprogram memory 129) read from the selected row 1528 specified by theaddress 1522 and from the location specified by the address 1522 withinthe selected row 1528, as shown.

NNU Internal RAM Port Configurations

Referring now to FIG. 16, a block diagram illustrating an embodiment ofthe data RAM 122 of FIG. 1 is shown. The data RAM 122 includes a memoryarray 1606, a read port 1602 and a write port 1604. The memory array1606 holds the data words and is preferably arranged as D rows of Nwords, as described above. In one embodiment, the memory array 1606comprises an array of 64 horizontally arranged static RAM cells in whicheach cell is 128 bits wide and 64 tall to provide a 64 KB data RAM 122that is 8192 bits wide and has 64 rows, and the data RAM 122 occupiesapproximately 0.2 square millimeters of die area. However, otherembodiments are contemplated.

The read port 1602 is coupled, preferably in a multiplexed fashion, tothe NPUs 126 and to the media registers 118. (More precisely, the mediaregisters 118 may be coupled to the read port 1602 via result bussesthat may also provide data to a reorder buffer and/or result forwardingbusses to the other execution units 112). The NPUs 126 and mediaregisters 118 share the read port 1602 to read the data RAM 122. Thewrite port 1604 is also coupled, preferably in a multiplexed fashion, tothe NPUs 126 and to the media registers 118. The NPUs 126 and mediaregisters 118 shared the write port 1604 to write the data RAM 122.Thus, advantageously, the media registers 118 can concurrently write tothe data RAM 122 while the NPUs 126 are also reading from the data RAM122, or the NPUs 126 can concurrently write to the data RAM 122 whilethe media registers 118 are reading from the data RAM 122. This mayadvantageously provide improved performance. For example, the NPUs 126can read the data RAM 122 (e.g., to continue to perform calculations)while the media registers 118 write more data words to the data RAM 122.For another example, the NPUs 126 can write calculation results to thedata RAM 122 while the media registers 118 read calculation results fromthe data RAM 122. In one embodiment, the NPUs 126 can write a row ofcalculation results to the data RAM 122 while the NPUs 126 also read arow of data words from the data RAM 122. In one embodiment, the memoryarray 1606 is configured in banks. When the NPUs 126 access the data RAM122, all of the banks are activated to access an entire row of thememory array 1606; whereas, when the media registers 118 access the dataRAM 122, only the specified banks are activated. In one embodiment, eachbank is 128 bits wide and the media registers 118 are 256 bits wide,hence two banks are activated per media register 118 access, forexample. In one embodiment, one of the ports 1602/1604 is a read/writeport. In one embodiment, both the ports 1602 and 1604 are read/writeports.

An advantage of the rotater capability of the NPUs 126 as describedherein is that it facilitates the ability for the memory array 1606 ofthe data RAM 122 to have significantly fewer rows, and therefore berelatively much smaller, than might otherwise be needed in order toinsure that the NPUs 126 are highly utilized, which requires thearchitectural program (via the media registers 118) to be able tocontinue to provide data to the data RAM 122 and to retrieve resultsfrom it while the NPUs 126 are performing computations.

Internal RAM Buffer

Referring now to FIG. 17, a block diagram illustrating an embodiment ofthe weight RAM 124 of FIG. 1 and a buffer 1704 is shown. The weight RAM124 includes a memory array 1706 and a port 1702. The memory array 1706holds the weight words and is preferably arranged as W rows of N words,as described above. In one embodiment, the memory array 1706 comprisesan array of 128 horizontally arranged static RAM cells in which eachcell is 64 bits wide and 2048 tall to provide a 2 MB weight RAM 124 thatis 8192 bits wide and has 2048 rows, and the weight RAM 124 occupiesapproximately 2.4 square millimeters of die area. However, otherembodiments are contemplated.

The port 1702 is coupled, preferably in a multiplexed fashion, to theNPUs 126 and to the buffer 1704. The NPUs 126 and buffer 1704 read andwrite the weight RAM 124 via the port 1702. The buffer 1704 is alsocoupled to the media registers 118 of FIG. 1 such that the mediaregisters 118 read and write the weight RAM 124 through the buffer 1704.Thus, advantageously, the media registers 118 can concurrently write toor read from the buffer 1704 while the NPUs 126 are also reading from orwriting to the weight RAM 124 (although preferably the NPUs 126 stall,if they are currently executing, to avoid accessing the weight RAM 124while the buffer 1704 is accessing the weight RAM 124). This mayadvantageously provide improved performance, particularly since thereads/writes by the media registers 118 to the weight RAM 124 arerelatively much smaller than the reads/writes by the NPUs 126 to theweight RAM 124. For example, in one embodiment, the NPUs 126 read/write8192 bits (one row) at a time, whereas the media registers 118 are 256bits wide, and each MTNN instructions 1400 writes two media registers118, i.e., 512 bits. Thus, in the case where the architectural programexecutes sixteen MTNN instructions 1400 to populate the buffer 1704, aconflict occurs between the NPUs 126 and the architectural program foraccess to the weight RAM 124 only less than approximately six percent ofthe time. In an alternate embodiment, the instruction translator 104translates a MTNN instruction 1400 into two microinstructions 105, eachof which writes a single media register 118 to the buffer 1704, in whichcase a conflict occurs between the NPUs 126 and the architecturalprogram for access to the weight RAM 124 even less frequently.

In one embodiment that includes the buffer 1704, writing to the weightRAM 124 by an architectural program requires multiple MTNN instructions1400. One or more MTNN instructions 1400 specify a function 1432 towrite to specified chunks of the buffer 1704 followed by an MTNNinstruction 1400 that specifies a function 1432 that instructs the NNU121 to write the contents of the buffer 1704 to a specified row of theweight RAM 124, where the size of a chunk is twice the number of bits ofa media register 118 and chunks are naturally aligned within the buffer1704. In one embodiment, in each of the MTNN instructions 1400 thatspecify a function 1432 to write to specified chunks of the buffer 1704,a bitmask is included that has a bit corresponding to each chunk of thebuffer 1704. The data from the two specified source registers 118 iswritten to each chunk of the buffer 1704 whose corresponding bit in thebitmask is set. This may be useful for repeated data values within a rowof the weight RAM 124. For example, in order to zero out the buffer 1704(and subsequently a row of the weight RAM 124), the programmer may loadthe source registers with zero and set all bits of the bitmask.Additionally, the bitmask enables the programmer to only write toselected chunks of the buffer 1704 and thereby retain the previous datain the other chunks.

In one embodiment that includes the buffer 1704, reading from the weightRAM 124 by an architectural program requires multiple MFNN instructions1500. An initial MFNN instruction 1500 specifies a function 1532 to loadthe buffer 1704 from a specified row of the weight RAM 124 followed byone or more MFNN instructions 1500 that specify a function 1532 to reada specified chunk of the buffer 1704 into the destination register,where the size of a chunk is the number of bits of a media register 118and chunks are naturally aligned within the buffer 1704. Otherembodiments are contemplated in which the weight RAM 124 includesmultiple buffers 1704 to further reduce contention between the NPUs 126and the architectural program for access to the weight RAM 124 byincreasing the number of accesses that can be made by the architecturalprogram while the NPUs 126 are executing, which may increase thelikelihood that the accesses by the buffers 1704 can be performed duringclock cycles in which the NPUs 126 do not need to access the weight RAM124.

Although FIG. 16 describes a dual-ported data RAM 122, other embodimentsare contemplated in which the weight RAM 124 is also dual-ported.Furthermore, although FIG. 17 describes a buffer for use with the weightRAM 124, other embodiments are contemplated in which the data RAM 122also has an associated buffer similar to buffer 1704.

Dynamically Configurable NPUs

Referring now to FIG. 18, a block diagram illustrating a dynamicallyconfigurable NPU 126 of FIG. 1 is shown. The NPU 126 of FIG. 18 issimilar in many respects to the NPU 126 of FIG. 2. However, the NPU 126of FIG. 18 is dynamically configurable to operate in one of twodifferent configurations. In a first configuration, the NPU 126 of FIG.18 operates similar to the NPU 126 of FIG. 2. That is, in the firstconfiguration, referred to herein as “wide” configuration or “single”configuration, the ALU 204 of the NPU 126 performs operations on asingle wide data word and a single wide weight word (e.g., 16 bits) togenerate a single wide result. In contrast, in the second configuration,referred to herein as “narrow” configuration or “dual” configuration,the NPU 126 performs operations on two narrow data words and tworespective narrow weight words (e.g., 8 bits) to generate two respectivenarrow results. In one embodiment, the configuration (wide or narrow) ofthe NPU 126 is made by the initialize NPU instruction (e.g., at address0 of FIG. 20, described below). Alternatively, the configuration is madeby an MTNN instruction whose function 1432 specifies to configure theNPU 126 to the configuration (wide or narrow). Preferably, configurationregisters are populated by the program memory 129 instruction or theMTNN instruction that determine the configuration (wide or narrow). Forexample, the configuration register outputs are provided to the ALU 204,AFU 212 and logic that generates the mux-reg control signal 213.Generally speaking, the elements of the NPUs 126 of FIG. 18 performsimilar functions to their like-numbered elements of FIG. 2 andreference should be made thereto for an understanding of FIG. 18.However, the embodiment of FIG. 18 will now be described, includingdifferences from FIG. 2.

The NPU 126 of FIG. 18 includes two registers 205A and 205B, two 3-inputmux-regs 208A and 208B, an ALU 204, two accumulators 202A and 202B, andtwo AFUs 212A and 212B. Each of the registers 205A/205B is separatelyhalf the width (e.g., 8 bits) of register 205 of FIG. 2. Each of theregisters 205A/205B receives a respective narrow weight word 206A/206B(e.g., 8 bits) from the weight RAM 124 and provides its output 203A/203Bon a subsequent clock cycle to operand selection logic 1898 of the ALU204. When the NPU 126 is in a wide configuration, the registers205A/205B effectively function together to receive a wide weight word206A/206B (e.g., 16 bits) from the weight RAM 124, similar to the mannerof the register 205 of the embodiment of FIG. 2; and when the NPU 126 isin a narrow configuration, the registers 205A/205B effectively functionindividually to each receive a narrow weight word 206A/206B (e.g., 8bits) from the weight RAM 124 such that the NPU 126 is effectively twoseparate narrow NPUs. Nevertheless, the same output bits of the weightRAM 124 are coupled to and provided to the registers 205A/205B,regardless of the configuration of the NPU 126. For example, theregister 205A of NPU 0 receives byte 0, the register 205B of NPU 0receives byte 1, the register 205A of NPU 1 receives byte 2, theregister 205A of NPU 1 receives byte 3, and so forth to the register205B of NPU 511 receives byte 1023.

Each of the mux-regs 208A/208B is separately half the width (e.g., 8bits) of register 208 of FIG. 2. The mux-reg 208A selects one of itsinputs 207A or 211A or 1811A to store in its register and then toprovide on its output 209A on a subsequent clock cycle, and the mux-reg208B selects one of its inputs 207B or 211B or 1811B to store in itsregister and then to provide on its output 209B on a subsequent clockcycle to the operand selection logic 1898. The input 207A receives anarrow data word (e.g., 8 bits) from the data RAM 122, and the input207B receives a narrow data word from the data RAM 122. When the NPU 126is in a wide configuration, the mux-regs 208A/208B effectively functiontogether to receive a wide data word 207A/207B (e.g., 16 bits) from thedata RAM 122, similar to the manner of the mux-reg 208 of the embodimentof FIG. 2; and when the NPU 126 is in a narrow configuration, themux-regs 208A/208B effectively function individually to each receive anarrow data word 207A/207B (e.g., 8 bits) from the data RAM 122 suchthat the NPU 126 is effectively two separate narrow NPUs. Nevertheless,the same output bits of the data RAM 122 are coupled to and provided tothe mux-regs 208A/208B, regardless of the configuration of the NPU 126.For example, the mux-reg 208A of NPU 0 receives byte 0, the mux-reg 208Bof NPU 0 receives byte 1, the mux-reg 208A of NPU 1 receives byte 2, themux-reg 208A of NPU 1 receives byte 3, and so forth to the mux-reg 208Bof NPU 511 receives byte 1023.

The input 211A receives the output 209A of mux-reg 208A of the adjacentNPU 126, and the input 211B receives the output 209B of mux-reg 208B ofthe adjacent NPU 126. The input 1811A receives the output 209B ofmux-reg 208B of the adjacent NPU 126, and the input 1811B receives theoutput 209A of mux-reg 208A of the instant NPU 126, as shown. The NPU126 shown in FIG. 18 is denoted NPU J from among the N NPUs 126 ofFIG. 1. That is, NPU J is a representative instance of the N NPUs 126.Preferably, the mux-reg 208A input 211A of NPU J receives the mux-reg208A output 209A of NPU 126 instance J−1, the mux-reg 208A input 1811Aof NPU J receives the mux-reg 208B output 209B of NPU 126 instance J−1,and the mux-reg 208A output 209A of NPU J is provided both to themux-reg 208A input 211A of NPU 126 instance J+1 and to the mux-reg 208Binput 211B of NPU 126 instance J; and the mux-reg 208B input 211B of NPUJ receives the mux-reg 208B output 209B of NPU 126 instance J−1, themux-reg 208B input 1811B of NPU J receives the mux-reg 208A output 209Aof NPU 126 instance J, and the mux-reg 208B output 209B of NPU J isprovided to both the mux-reg 208A input 1811A of NPU 126 instance J+1and to the mux-reg 208B input 211B of NPU 126 instance J+1.

The control input 213 controls which of the three inputs each of themux-regs 208A/208B selects to store in its respective register and thatis subsequently provided on the respective outputs 209A/209B. When theNPU 126 is instructed to load a row from the data RAM 122 (e.g., as bythe multiply-accumulate instruction at address 1 of FIG. 20, describedbelow), regardless of whether the NPU 126 is in a wide or narrowconfiguration, the control input 213 controls each of the mux-regs208A/208B to select a respective narrow data word 207A/207B (e.g., 8bits) from the corresponding narrow word of the selected row of the dataRAM 122.

When the NPU 126 is instructed to rotate the previously received datarow values (e.g., as by the multiply-accumulate rotate instruction ataddress 2 of FIG. 20, described below), if the NPU 126 is in a narrowconfiguration, the control input 213 controls each of the mux-regs208A/208B to select the respective input 1811A/1811B. In this case, themux-regs 208A/208B function individually effectively such that the NPU126 is effectively two separate narrow NPUs. In this manner, themux-regs 208A and 208B of the N NPUs 126 collectively operate as a2N-narrow-word rotater, as described in more detail below with respectto FIG. 19.

When the NPU 126 is instructed to rotate the previously received datarow values, if the NPU 126 is in a wide configuration, the control input213 controls each of the mux-regs 208A/208B to select the respectiveinput 211A/211B. In this case, the mux-regs 208A/208B function togethereffectively as if the NPU 126 is a single wide NPU 126. In this manner,the mux-regs 208A and 208B of the N NPUs 126 collectively operate as anN-wide-word rotater, similar to the manner described with respect toFIG. 3.

The ALU 204 includes the operand selection logic 1898, a wide multiplier242A, a narrow multiplier 242B, a wide two-input mux 1896A, a narrowtwo-input mux 1896B, a wide adder 244A and a narrow adder 244B.Effectively, the ALU 204 comprises the operand selection logic 1898, awide ALU 204A (comprising the wide multiplier 242A, the wide mux 1896Aand the wide adder 244A) and a narrow ALU 204B (comprising the narrowmultiplier 242B, the narrow mux 1896B and the narrow adder 244B).Preferably, the wide multiplier 242A multiplies two wide words and issimilar to the multiplier 242 of FIG. 2, e.g., a 16-bit by 16-bitmultiplier. The narrow multiplier 242B multiplies two narrow words,e.g., an 8-bit by 8-bit multiplier that generates a 16-bit result. Whenthe NPU 126 is in a narrow configuration, the wide multiplier 242A iseffectively used, with the help of the operand selection logic 1898, asa narrow multiplier to multiply two narrow words so that the NPU 126effectively functions as two narrow NPUs. Preferably, the wide adder244A adds the output of the wide mux 1896A and the wide accumulator 202Aoutput 217A to generate a sum 215A for provision to the wide accumulator202A and is similar to the adder 244 of FIG. 2. The narrow adder 244Badds the output of the narrow mux 1896B and the narrow accumulator 202Boutput 217B to generate a sum 215B for provision to the narrowaccumulator 202B. In one embodiment, the narrow accumulator 202B is 28bits wide to avoid loss of precision in the accumulation of up to 102416-bit products. When the NPU 126 is in a wide configuration, the narrowmultiplier 242B, narrow mux 1896B, narrow adder 244B, narrow accumulator202B and narrow AFU 212B are preferably inactive to reduce powerconsumption.

The operand selection logic 1898 selects operands from 209A, 209B, 203Aand 203B to provide to the other elements of the ALU 204, as describedin more detail below. Preferably, the operand selection logic 1898 alsoperforms other functions, such as performing sign extension ofsigned-valued data words and weight words. For example, if the NPU 126is in a narrow configuration, the operand selection logic 1898 signextends the narrow data word and weight word to the width of a wide wordbefore providing them to the wide multiplier 242A. Similarly, if the ALU204 is instructed to pass through a narrow data/weight word (bypass thewide multiplier 242A via wide mux 1896A), the operand selection logic1898 sign extends the narrow data/weight word to the width of a wideword before providing it to the wide adder 244A. Preferably, logic isalso present in the ALU 204 of the NPU 126 of FIG. 2 to perform thesign-extension function.

The wide mux 1896A receives the output of the wide multiplier 242A andan operand from the operand selection logic 1898 and selects one of theinputs for provision to the wide adder 244A, and the narrow mux 1896Breceives the output of the narrow multiplier 242B and an operand fromthe operand selection logic 1898 and selects one of the inputs forprovision to the narrow adder 244B.

The operands provided by the operand selection logic 1898 depend uponthe configuration of the NPU 126 and upon the arithmetic and/or logicaloperations to be performed by the ALU 204 based on the functionspecified by the instruction being executed by the NPU 126. For example,if the instruction instructs the ALU 204 to perform amultiply-accumulate and the NPU 126 is in a wide configuration, theoperand selection logic 1898 provides to the wide multiplier 242A on oneinput a wide word that is the concatenation of outputs 209A and 209B andon the other input a wide word that is the concatenation of outputs 203Aand 203B, and the narrow multiplier 242B is inactive, so that the NPU126 functions as a single wide NPU 126 similar to the NPU 126 of FIG. 2.Whereas, if the instruction instructs the ALU 204 to perform amultiply-accumulate and the NPU 126 is in a narrow configuration, theoperand selection logic 1898 provides to the wide multiplier 242A on oneinput an extended, or widened, version of the narrow data word 209A andon the other input an extended version of the narrow weight word 203A;additionally, the operand selection logic 1898 provides to the narrowmultiplier 242B on one input the narrow data words 209B and on the otherinput the narrow weight word 203B. To extend, or widen, a narrow word,if the narrow word is signed, then the operand selection logic 1898sign-extends the narrow word, whereas if the narrow word is unsigned,the operand selection logic 1898 pads the narrow word with zero-valuedupper bits.

For another example, if the NPU 126 is in a wide configuration and theinstruction instructs the ALU 204 to perform an accumulate of the weightword, the wide multiplier 242A is bypassed and the operand selectionlogic 1898 provides the concatenation of outputs 203A and 203B to thewide mux 1896A for provision to the wide adder 244A. Whereas, if the NPU126 is in a narrow configuration and the instruction instructs the ALU204 to perform an accumulate of the weight word, the wide multiplier242A is bypassed and the operand selection logic 1898 provides anextended version of the output 203A to the wide mux 1896A for provisionto the wide adder 244A; and the narrow multiplier 242B is bypassed andthe operand selection logic 1898 provides an extended version of theoutput 203B to the narrow mux 1896B for provision to the narrow adder244B.

For another example, if the NPU 126 is in a wide configuration and theinstruction instructs the ALU 204 to perform an accumulate of the dataword, the wide multiplier 242A is bypassed and the operand selectionlogic 1898 provides the concatenation of outputs 209A and 209B to thewide mux 1896A for provision to the wide adder 244A. Whereas, if the NPU126 is in a narrow configuration and the instruction instructs the ALU204 to perform an accumulate of the data word, the wide multiplier 242Ais bypassed and the operand selection logic 1898 provides an extendedversion of the output 209A to the wide mux 1896A for provision to thewide adder 244A; and the narrow multiplier 242B is bypassed and theoperand selection logic 1898 provides an extended version of the output209B to the narrow mux 1896B for provision to the narrow adder 244B. Theaccumulation of weight/data words may be useful for performing averagingoperations that are used in pooling layers of some artificial neuralnetwork applications, such as image processing.

Preferably, the NPU 126 also includes a second wide mux (not shown) forbypassing the wide adder 244A to facilitate loading the wide accumulator202A with a wide data/weight word in wide configuration or an extendednarrow data/weight word in narrow configuration, and a second narrow mux(not shown) for bypassing the narrow adder 244B to facilitate loadingthe narrow accumulator 202B with a narrow data/weight word in narrowconfiguration. Preferably, the ALU 204 also includes wide and narrowcomparator/mux combinations (not shown) that receive the respectiveaccumulator value 217A/217B and respective mux 1896A/1896B output toselect the maximum value between the accumulator value 217A/217B and adata/weight word 209A/B/203A/B, an operation that is used in poolinglayers of some artificial neural network applications, as described inmore detail below, e.g., with respect to FIGS. 27 and 28. Additionally,the operand selection logic 1898 is configured to provide zero-valuedoperands (for addition with zero or for clearing the accumulators) andto provide one-valued operands (for multiplication by one).

The narrow AFU 212B receives the output 217B of the narrow accumulator202B and performs an activation function on it to generate a narrowresult 133B, and the wide AFU 212A receives the output 217A of the wideaccumulator 202A and performs an activation function on it to generate awide result 133A. When the NPU 126 is in a narrow configuration, thewide AFU 212A considers the output 217A of the wide accumulator 202Aaccordingly and performs an activation function on it to generate anarrow result, e.g., 8 bits, as described in more detail below withrespect to FIGS. 29A through 30, for example.

As may observed from the above description, advantageously the singleNPU 126 operates effectively as two narrow NPUs when in a narrowconfiguration, thus providing, for smaller words, approximately up totwice the throughput as when in the wide configuration. For example,assume a neural network layer having 1024 neurons each receiving 1024narrow inputs from the previous layer (and having narrow weight words),resulting in 1 Mega-connections. An NNU 121 having 512 NPUs 126 in anarrow configuration (1024 narrow NPU) compared to an NNU 121 having 512NPUs 126 in a wide configuration is capable of processing four times thenumber of connections (1 Mega-connections vs. 256K connections) inapproximately half the time (approx. 1026 clocks vs. 514 clocks), albeitfor narrow words rather than wide words.

In one embodiment, the dynamically configurable NPU 126 of FIG. 18includes 3-input multiplexed-registers similar to mux-regs 208A and 208Bin place of the registers 205A and 205B to accomplish a rotater for arow of weight words received from the weight RAM 124 somewhat similar tothe manner described with respect to the embodiment of FIG. 7 but in adynamically configurable fashion as described with respect to FIG. 18.

Referring now to FIG. 19, a block diagram illustrating an embodiment ofthe arrangement of the 2N mux-regs 208A/208B of the N NPUs 126 of theNNU 121 of FIG. 1 according to the embodiment of FIG. 18 to illustratetheir operation as a rotater for a row of data words 207 received fromthe data RAM 122 of FIG. 1 is shown. In the embodiment of FIG. 19, N is512 such that the NNU 121 has 1024 mux-regs 208A/208B, denoted 0 through511, corresponding to 512 NPUs 126 and effectively 1024 narrow NPUs, asshown. The two narrow NPUs within a NPU 126 are denoted A and B, andwithin each of the mux-regs 208, the designation of the correspondingnarrow NPU is shown. More specifically, mux-reg 208A of NPU 126 0 isdesignated 0-A, mux-reg 208B of NPU 126 0 is designated 0-B, mux-reg208A of NPU 126 1 is designated 1-A, mux-reg 208B of NPU 126 1 isdesignated 1-B, mux-reg 208A of NPU 126 511 is designated 511-A, andmux-reg 208B of NPU 126 511 is designated 0-B, which values alsocorrespond to the narrow NPUs of FIG. 21 described below.

Each mux-reg 208A receives its corresponding narrow data word 207A ofone row of the D rows of the data RAM 122, and each mux-reg 208Breceives its corresponding narrow data word 207B of one row of the Drows of the data RAM 122. That is, mux-reg 0A receives narrow data word0 of the data RAM 122 row, mux-reg 0B receives narrow data word 1 of thedata RAM 122 row, mux-reg 1A receives narrow data word 2 of the data RAM122 row, mux-reg 1B receives narrow data word 3 of the data RAM 122 row,and so forth to mux-reg 511A receives narrow data word 1022 of the dataRAM 122 row, and mux-reg 511B receives narrow data word 1023 of the dataRAM 122 row. Additionally, mux-reg 1A receives on its input 211A theoutput 209A of mux-reg 0A, mux-reg 1B receives on its input 211B theoutput 209B of mux-reg 0B, and so forth to mux-reg 511A that receives onits input 211A the output 209A of mux-reg 510A and mux-reg 511B thatreceives on its input 211B the output 209B of mux-reg 510B, and mux-reg0A receives on its input 211A the output 209A of mux-reg 511A andmux-reg 0B receives on its input 211B the output 209B of mux-reg 511B.Each of the mux-regs 208A/208B receives the control input 213 thatcontrols whether to select the data word 207A/207B or the rotated input211A/211B or the rotated input 1811A/1811B. Finally, mux-reg 1A receiveson its input 1811A the output 209B of mux-reg 0B, mux-reg 1B receives onits input 1811B the output 209A of mux-reg 1A, and so forth to mux-reg511A that receives on its input 1811A the output 209B of mux-reg 510Band mux-reg 511B that receives on its input 1811B the output 209A ofmux-reg 511A, and mux-reg 0A receives on its input 1811A the output 209Bof mux-reg 511B and mux-reg 0B receives on its input 1811B the output209A of mux-reg 0A. Each of the mux-regs 208A/208B receives the controlinput 213 that controls whether to select the data word 207A/207B or therotated input 211A/211B or the rotated input 1811A/1811B. As describedin more detail below, in one mode of operation, on a first clock cycle,the control input 213 controls each of the mux-regs 208A/208B to selectthe data word 207A/207B for storage in the register and for subsequentprovision to the ALU 204; and during subsequent clock cycles (e.g., M-1clock cycles as described above), the control input 213 controls each ofthe mux-regs 208A/208B to select the rotated input 1811A/1811B forstorage in the register and for subsequent provision to the ALU 204.

Referring now to FIG. 20, a table illustrating a program for storage inthe program memory 129 of and execution by the NNU 121 of FIG. 1 havingNPUs 126 according to the embodiment of FIG. 18 is shown. The exampleprogram of FIG. 20 is similar in many ways to the program of FIG. 4.However, differences will now be described. The initialize NPUinstruction at address 0 specifies that the NPU 126 is to be in a narrowconfiguration. Additionally, the multiply-accumulate rotate instructionat address 2 specifies a count of 1023 and requires 1023 clock cycles,as shown. This is because the example of FIG. 20 assumes effectively1024 narrow (e.g., 8-bit) neurons (NPUs) of a layer, each having 1024connection inputs from a previous layer of 1024 neurons, for a total of1024K connections. Each neuron receives an 8-bit data value from eachconnection input and multiplies the 8-bit data value by an appropriate8-bit weight value.

Referring now to FIG. 21, a timing diagram illustrating the execution ofthe program of FIG. 20 by the NNU 121 that includes NPUs 126 of FIG. 18operating in a narrow configuration is shown. The timing diagram of FIG.21 is similar in many ways to the timing diagram of FIG. 5; however,differences will now be described.

In the timing diagram of FIG. 21, the NPUs 126 are in a narrowconfiguration because the initialize NPU instruction at address 0initializes them to a narrow configuration. Consequently, the 512 NPUs126 effectively operate as 1024 narrow NPUs (or neurons), which aredesignated in the columns as NPU 0-A and NPU 0-B (the two narrow NPUs ofNPU 126 0), NPU 1-A and NPU 1-B (the two narrow NPUs of NPU 126 1) andso forth through NPU 511-A and NPU 511-B (the two narrow NPUs of NPU 126511). For simplicity and clarity of illustration, the operations onlyfor narrow NPUs 0-A, 0-B and 511-B are shown. Due to the fact that themultiply-accumulate rotate at address 2 specifies a count of 1023, whichrequires 1023 clocks, the rows of the timing diagram of FIG. 21 includeup to clock cycle 1026.

At clock 0, each of the 1024 NPUs performs the initializationinstruction of FIG. 4, which is illustrated in FIG. 5 by the assignmentof a zero value to the accumulator 202.

At clock 1, each of the 1024 narrow NPUs performs themultiply-accumulate instruction at address 1 of FIG. 20. Narrow NPU 0-Aaccumulates the accumulator 202A value (which is zero) with the productof data RAM 122 row 17 narrow word 0 and weight RAM 124 row 0 narrowword 0; narrow NPU 0-B accumulates the accumulator 202B value (which iszero) with the product of data RAM 122 row 17 narrow word 1 and weightRAM 124 row 0 narrow word 1; and so forth to narrow NPU 511-Baccumulates the accumulator 202B value (which is zero) with the productof data RAM 122 row 17 narrow word 1023 and weight RAM 124 row 0 narrowword 1023, as shown.

At clock 2, each of the 1024 narrow NPUs performs a first iteration ofthe multiply-accumulate rotate instruction at address 2 of FIG. 20.Narrow NPU 0-A accumulates the accumulator 202A value 217A with theproduct of the rotated narrow data word 1811A received from the mux-reg208B output 209B of narrow NPU 511-B (which was narrow data word 1023received from the data RAM 122) and weight RAM 124 row 1 narrow word 0;narrow NPU 0-B accumulates the accumulator 202B value 217B with theproduct of the rotated narrow data word 1811B received from the mux-reg208A output 209A of narrow NPU 0-A (which was narrow data word 0received from the data RAM 122) and weight RAM 124 row 1 narrow word 1;and so forth to narrow NPU 511-B accumulates the accumulator 202B value217B with the product of the rotated narrow data word 1811B receivedfrom the mux-reg 208A output 209A of narrow NPU 511-A (which was narrowdata word 1022 received from the data RAM 122) and weight RAM 124 row 1narrow word 1023, as shown.

At clock 3, each of the 1024 narrow NPUs performs a second iteration ofthe multiply-accumulate rotate instruction at address 2 of FIG. 20.Narrow NPU 0-A accumulates the accumulator 202A value 217A with theproduct of the rotated narrow data word 1811A received from the mux-reg208B output 209B of narrow NPU 511-B (which was narrow data word 1022received from the data RAM 122) and weight RAM 124 row 2 narrow word 0;narrow NPU 0-B accumulates the accumulator 202B value 217B with theproduct of the rotated narrow data word 1811B received from the mux-reg208A output 209A of narrow NPU 0-A (which was narrow data word 1023received from the data RAM 122) and weight RAM 124 row 2 narrow word 1;and so forth to narrow NPU 511-B accumulates the accumulator 202B value217B with the product of the rotated narrow data word 1811B receivedfrom the mux-reg 208A output 209A of narrow NPU 511-A (which was narrowdata word 1021 received from the data RAM 122) and weight RAM 124 row 2narrow word 1023, as shown. As indicated by the ellipsis of FIG. 21,this continues for each of the following 1021 clock cycles until . . .

At clock 1024, each of the 1024 narrow NPUs performs a 1023^(rd)iteration of the multiply-accumulate rotate instruction at address 2 ofFIG. 20. Narrow NPU 0-A accumulates the accumulator 202A value 217A withthe product of the rotated narrow data word 1811A received from themux-reg 208B output 209B of narrow NPU 511-B (which was narrow data word1 received from the data RAM 122) and weight RAM 124 row 1023 narrowword 0; NPU 0-B accumulates the accumulator 202B value 217B with theproduct of the rotated narrow data word 1811B received from the mux-reg208A output 209A of NPU 0-A (which was narrow data word 2 received fromthe data RAM 122) and weight RAM 124 row 1023 narrow word 1; and soforth to NPU 511-B accumulates the accumulator 202B value with theproduct of the rotated narrow data word 1811B received from the mux-reg208A output 209A of NPU 511-A (which was narrow data word 0 receivedfrom the data RAM 122) and weight RAM 124 row 1023 narrow word 1023, asshown.

At clock 1025, the AFU 212A/212B of each of the 1024 narrow NPUsperforms the activation function instruction at address 3 of FIG. 20.Finally, at clock 1026, each of the 1024 narrow NPUs performs the writeAFU output instruction at address 4 of FIG. 20 by writing back itsnarrow result 133A/133B to its corresponding narrow word of row 16 ofthe data RAM 122, i.e., the narrow result 133A of NPU 0-A is written tonarrow word 0 of the data RAM 122, the narrow result 133B of NPU 0-B iswritten to narrow word 1 of the data RAM 122, and so forth to the narrowresult 133 of NPU 511-B is written to narrow word 1023 of the data RAM122. The operation described above with respect to FIG. 21 is also shownin block diagram form in FIG. 22.

Referring now to FIG. 22, a block diagram illustrating the NNU 121 ofFIG. 1 including the NPUs 126 of FIG. 18 to execute the program of FIG.20 is shown. The NNU 121 includes the 512 NPUs 126, i.e., 1024 narrowNPUs, the data RAM 122 that receives its address input 123, and theweight RAM 124 that receives its address input 125. Although not shown,on clock 0 the 1024 narrow NPUs perform the initialization instructionof FIG. 20. As shown, on clock 1, the 1024 8-bit data words of row 17are read out of the data RAM 122 and provided to the 1024 narrow NPUs.On clocks 1 through 1024, the 1024 8-bit weight words of rows 0 through1023, respectively, are read out of the weight RAM 124 and provided tothe 1024 narrow NPUs. Although not shown, on clock 1, the 1024 narrowNPUs perform their respective multiply-accumulate operations on theloaded data words and weight words. On clocks 2 through 1024, themux-regs 208A/208B of the 1024 narrow NPUs operate as a 1024 8-bit wordrotater to rotate the previously loaded data words of row 17 of the dataRAM 122 to the adjacent narrow NPU, and the narrow NPUs perform themultiply-accumulate operation on the respective rotated data narrow wordand the respective narrow weight word loaded from the weight RAM 124.Although not shown, on clock 1025, the 1024 narrow AFUs 212A/212Bperform the activation instruction. On clock 1026, the 1024 narrow NPUswrite back their respective 1024 8-bit results 133A/133B to row 16 ofthe data RAM 122.

As may be observed, the embodiment of FIG. 18 may be advantageous overthe embodiment of FIG. 2, for example, because it provides theflexibility for the programmer to perform computations using wide dataand weight words (e.g., 16-bits) when that amount of precision is neededby the particular application being modeled and narrow data and weightwords (e.g., 8-bits) when that amount of precision is needed by theapplication. From one perspective, the embodiment of FIG. 18 providesdouble the throughput over the embodiment of FIG. 2 for narrow dataapplications at the cost of the additional narrow elements (e.g.,mux-reg 208B, reg 205B, narrow ALU 204B, narrow accumulator 202B, narrowAFU 212B), which is approximately a 50% increase in area of the NPU 126.

Tri-Mode NPUs

Referring now to FIG. 23, a block diagram illustrating a dynamicallyconfigurable NPU 126 of FIG. 1 according to an alternate embodiment isshown. The NPU 126 of FIG. 23 is configurable not only in wide andnarrow configurations, but also in a third configuration referred toherein as a “funnel” configuration. The NPU 126 of FIG. 23 is similar inmany respects to the NPU 126 of FIG. 18. However, the wide adder 244A ofFIG. 18 is replaced in the NPU 126 of FIG. 23 with a 3-input wide adder2344A that receives a third addend 2399 that is an extended version ofthe output of the narrow mux 1896B. A program for operating an NNU 121having the NPUs 126 of FIG. 23 is similar in most respects to theprogram of FIG. 20. However, the initialize NPU instruction at address 0initializes the NPUs 126 to a funnel configuration, rather than a narrowconfiguration. Additionally, the count of the multiply-accumulate rotateinstruction at address 2 is 511 rather than 1023.

When in the funnel configuration, the NPU 126 operates similarly to whenin the narrow configuration when executing a multiply-accumulateinstruction such as at address 1 of FIG. 20 in that it receives twonarrow data words 207A/207B and two narrow weight words 206A/206B; thewide multiplier 242A multiplies data word 209A and weight word 203A togenerate product 246A which the wide mux 1896A selects; and the narrowmultiplier 242B multiplies data word 209B and weight word 203B togenerate product 246B which the narrow mux 1896B selects. However, thewide adder 2344A adds both the product 246A (selected by wide mux 1896A)and the product 246B/2399 (selected by wide mux 1896B) to the wideaccumulator 202A value 217A, and narrow adder 244B and narrowaccumulator 202B are inactive. Furthermore, when in the funnelconfiguration, when executing a multiply-accumulate rotate instructionsuch as at address 2 of FIG. 20, the control input 213 causes themux-regs 208A/208B to rotate by two narrow words (e.g., 16-bits), i.e.,the mux-regs 208A/208B select their respective 211A/211B inputs as ifthey were in a wide configuration. However, the wide multiplier 242Amultiplies data word 209A and weight word 203A to generate product 246Awhich the wide mux 1896A selects; and the narrow multiplier 242Bmultiplies data word 209B and weight word 203B to generate product 246Bwhich the narrow mux 1896B selects; and the wide adder 2344A adds boththe product 246A (selected by wide mux 1896A) and the product 246B/2399(selected by wide mux 1896B) to the wide accumulator 202A value 217A,and the narrow adder 244B and narrow accumulator 202B are inactive asdescribed above. Finally, when in the funnel configuration, whenexecuting an activation function instruction such as at address 3 ofFIG. 20, the wide AFU 212A performs the activation function on theresulting sum 215A to generate a narrow result 133A and the narrow AFU212B is inactive. Hence, only the A narrow NPUs generate a narrow result133A, and the narrow results 133B generated by the B narrow NPUs areinvalid. Consequently, the row of results written back (e.g., to row 16as at the instruction at address 4 of FIG. 20) includes holes since onlythe narrow results 133A are valid and the narrow results 133B areinvalid. Thus, conceptually, each clock cycle each neuron (NPU 126 ofFIG. 23) processes two connection data inputs, i.e., multiplies twonarrow data words by their respective weights and accumulates the twoproducts, in contrast to the embodiments of FIGS. 2 and 18 which eachprocess a single connection data input per clock cycle.

As may be observed with respect to the embodiment of FIG. 23, the numberof result words (neuron outputs) produced and written back to the dataRAM 122 or weight RAM 124 is half the square root of the number of datainputs (connections) received and the written back row of results hasholes, i.e., every other narrow word result is invalid, morespecifically, the B narrow NPU results are not meaningful. Thus, theembodiment of FIG. 23 may be particularly efficient in neural networkshaving two successive layers in which, for example, the first layer hastwice as many neurons as the second layer (e.g., the first layer has1024 neurons fully connected to a second layer of 512 neurons).Furthermore, the other execution units 112 (e.g., media units, such asx86 AVX units) may perform pack operations on a disperse row of results(i.e., having holes) to make compact it (i.e., without holes), ifnecessary, for use in subsequent computations while the NNU 121 isperforming other computations associated with other rows of the data RAM122 and/or weight RAM 124.

Hybrid NNU Operation; Convolution and Pooling Capabilities

An advantage of the NNU 121 according to embodiments described herein isthat the NNU 121 is capable of concurrently operating in a fashion thatresembles a coprocessor in that it executes its own internal program andoperating in a fashion that resembles an execution unit of a processorin that it executes architectural instructions (or microinstructionstranslated therefrom) issued to it. The architectural instructions areof an architectural program being performed by the processor thatincludes the NNU 121. In this manner, the NNU 121 operates in a hybridfashion, which is advantageous because it provides the ability tosustain high utilization of the NNU 121. For example, the FIGS. 24through 26 illustrate the operation of the NNU 121 to perform aconvolution operation in which the NNU 121 is highly utilized, and FIGS.27 through 28 illustrate the operation of the NNU 121 to perform apooling operation, which are required for convolution layers and poolinglayers and other digital data computing applications, such as imageprocessing (e.g., edge detection, sharpening, blurring,recognition/classification). However, the hybrid operation of the NNU121 is not limited to performing a convolution or pooling operation,rather the hybrid feature may be used to perform other operations, suchas classic neural network multiply-accumulate and activation functionoperations as described above with respect to FIGS. 4 through 13. Thatis, the processor 100 (more specifically, the reservation stations 108)issue MTNN 1400 and MFNN 1500 instructions to the NNU 121 in response towhich the NNU 121 writes data to the memories 122/124/129 and readsresults from the memories 122/124 written there by the NNU 121, whileconcurrently the NNU 121 reads and writes the memories 122/124/129 inresponse to executing programs written to the program memory 129 by theprocessor 100 (via MTNN 1400 instructions).

Referring now to FIG. 24, a block diagram illustrating an example ofdata structures used by the NNU 121 of FIG. 1 to perform a convolutionoperation are shown. The block diagram includes a convolution kernel2402, a data array 2404, and the data RAM 122 and weight RAM 124 ofFIG. 1. Preferably, the data array 2404 (e.g., of image pixels) is heldin system memory (not shown) attached to the processor 100 and loadedinto the weight RAM 124 of the NNU 121 by the processor 100 executingMTNN instructions 1400. A convolution operation is an operation thatconvolves a first matrix with a second matrix, the second matrixreferred to as a convolution kernel herein. As understood in the contextof the present disclosure, a convolution kernel is a matrix ofcoefficients, which may also be referred to as weights, parameters,elements or values. Preferably, the convolution kernel 2042 is staticdata of the architectural program being executed by the processor 100.

The data array 2404 is a two-dimensional array of data values, and eachdata value (e.g., an image pixel value) is the size of a word of thedata RAM 122 or weight RAM 124 (e.g., 16 bits or 8 bits). In theexample, the data values are 16-bit words and the NNU 121 is configuredas 512 wide configuration NPUs 126. Additionally, in the embodiment, theNPUs 126 include mux-regs for receiving the weight words 206 from theweight RAM 124, such as mux-reg 705 of FIG. 7, in order to perform thecollective rotater operation of a row of data values received from theweight RAM 124, as described in more detail below. In the example, thedata array 2404 is a 2560 column×1600 row pixel array. When thearchitectural program convolves the data array 2404 with the convolutionkernel 2402, it breaks the data array 2404 into 20 chunks, each chunkbeing a 512×400 data matrix 2406, as shown.

The convolution kernel 2042, in the example, is a 3×3 matrix ofcoefficients, or weights, or parameters, or elements. The first row ofcoefficients are denoted C0,0; C0,1; and C0,2; the second row ofcoefficients are denoted C1,0; C1,1; and C1,2; and the third row ofcoefficients are denoted C2,0; C2,1; and C2,2. For example, aconvolution kernel that may be used for performing edge detection hasthe following coefficients: 0, 1, 0, 1, −4, 1, 0, 1, 0. For anotherexample, a convolution kernel that may be used to Gaussian blur an imagehas the following coefficients: 1, 2, 1, 2, 4, 2, 1, 2, 1. In this case,a divide is typically performed on the final accumulated value, wherethe divisor is the sum of the absolute values of the elements of theconvolution kernel 2042, which is 16 in this example. For anotherexample, the divisor is the number of elements of the convolution kernel2042. For another example, the divisor is a value that compresses theconvolutions back within a desired range of values, and the divisor isdetermined from the values of the elements of the convolution kernel2042 and the desired range and the range of the input values of thematrix being convolved.

As shown in FIG. 24 and described in more detail with respect to FIG.25, the architectural program writes the data RAM 122 with thecoefficients of the convolution kernel 2042. Preferably, all the wordsof each of nine (the number of elements in the convolution kernel 2402)consecutive rows of the data RAM 122 are written with a differentelement of the convolution kernel 2402 in row-major order. That is, eachword of one row is written with the first coefficient C0,0; the next rowis written with the second coefficient C0,1; the next row is writtenwith the third coefficient C0,2; the next row is written with the fourthcoefficient C1,0; and so forth until each word of the ninth row iswritten with the ninth coefficient C2,2, as shown. To convolve a datamatrix 2406 of a chunk of the data array 2404, the NPUs 126 repeatedlyread, in order, the nine rows of the data RAM 122 that hold theconvolution kernel 2042 coefficients, as described in more detail below,particularly with respect to FIG. 26A.

As shown in FIG. 24 and described in more detail with respect to FIG.25, the architectural program writes the weight RAM 124 with the valuesof a data matrix 2406. As the NNU program performs the convolution, itwrites back the resulting matrix to the weight RAM 124. Preferably, thearchitectural program writes a first data matrix 2406 to the weight RAM124 and starts the NNU 121, and while the NNU 121 is convolving thefirst data matrix 2406 with the convolution kernel 2042, thearchitectural program writes a second data matrix 2406 to the weight RAM124 so that as soon as the NNU 121 completes the convolution of thefirst data matrix 2406, the NNU 121 can start convolving the second datamatrix 2406, as described in more detail with respect to FIG. 25. Inthis manner, the architectural program ping-pongs back and forth betweenthe two regions of the weight RAM 124 in order to keep the NNU 121 fullyutilized. Thus, the example of FIG. 24 shows a first data matrix 2406Acorresponding to a first chunk occupying rows 0 through 399 of theweight RAM 124, and a second data matrix 2406B corresponding to a secondchunk occupying rows 500 through 899 of the weight RAM 124. Furthermore,as shown, the NNU 121 writes back the results of the convolutions torows 900-1299 and 1300-1699 of the weight RAM 124, which thearchitectural program subsequently reads out of the weight RAM 124. Thedata values of the data matrix 2406 held in the weight RAM 124 aredenoted “Dx,y” where “x” is the weight RAM 124 row number and “y” is theword, or column, number of the weight RAM 124. Thus, for example, dataword 511 in row 399 is denoted D399, 511 in FIG. 24, which is receivedby the mux-reg 705 of NPU 511.

Referring now to FIG. 25, a flowchart illustrating operation of theprocessor 100 of FIG. 1 to perform an architectural program that usesthe NNU 121 to perform a convolution of the convolution kernel 2042 withthe data array 2404 of FIG. 24. Flow begins at block 2502.

At block 2502, the processor 100, i.e., the architectural programrunning on the processor 100, writes the convolution kernel 2042 of FIG.24 to the data RAM 122 in the manner shown and described with respect toFIG. 24. Additionally, the architectural program initializes a variableN to a value of 1. The variable N denotes the current chunk of the dataarray 2404 being processed by the NNU 121. Additionally, thearchitectural program initializes a variable NUM_CHUNKS to a value of20. Flow proceeds to block 2504.

At block 2504, the processor 100 writes the data matrix 2406 for chunk 1to the weight RAM 124, as shown in FIG. 24 (e.g., data matrix 2406A ofchunk 1). Flow proceeds to block 2506.

At block 2506, the processor 100 writes a convolution program to the NNU121 program memory 129, using MTNN 1400 instructions that specify afunction 1432 to write the program memory 129. The processor 100 thenstarts the NNU convolution program using a MTNN 1400 instruction thatspecifies a function 1432 to start execution of the program. An exampleof the NNU convolution program is described in more detail with respectto FIG. 26A. Flow proceeds to decision block 2508.

At decision block 2508, the architectural program determines whether thevalue of variable N is less than NUM_CHUNKS. If so, flow proceeds toblock 2512; otherwise, flow proceeds to block 2514.

At block 2512, the processor 100 writes the data matrix 2406 for chunkN+1 to the weight RAM 124, as shown in FIG. 24 (e.g., data matrix 2406Bof chunk 2). Thus, advantageously, the architectural program writes thedata matrix 2406 for the next chunk to the weight RAM 124 while the NNU121 is performing the convolution on the current chunk so that the NNU121 can immediately start performing the convolution on the next chunkonce the convolution of the current chunk is complete, i.e., written tothe weight RAM 124. Flow proceeds to block 2514.

At block 2514, the processor 100 determines that the currently runningNNU program (started at block 2506 in the case of chunk 1, and startedat block 2518 in the case of chunks 2-20) has completed. Preferably, theprocessor 100 determines this by executing a MFNN 1500 instruction toread the NNU 121 status register 127. In an alternate embodiment, theNNU 121 generates an interrupt to indicate it has completed theconvolution program. Flow proceeds to decision block 2516.

At decision block 2516, the architectural program determines whether thevalue of variable N is less than NUM_CHUNKS. If so, flow proceeds toblock 2518; otherwise, flow proceeds to block 2522.

At block 2518, the processor 100 updates the convolution program so thatit can convolve chunk N+1. More specifically, the processor 100 updatesthe weight RAM 124 row value of the initialize NPU instruction ataddress 0 to the first row of the data matrix 2406 (e.g., to row 0 fordata matrix 2406A or to row 500 for data matrix 2406B) and updates theoutput row (e.g., to 900 or 1300). The processor 100 then starts theupdated NNU convolution program. Flow proceeds to block 2522.

At block 2522, the processor 100 reads the results of the NNUconvolution program from the weight RAM 124 for chunk N. Flow proceedsto decision block 2524.

At decision block 2524, the architectural program determines whether thevalue of variable N is less than NUM_CHUNKS. If so, flow proceeds toblock 2526; otherwise, flow ends.

At block 2526, the architectural program increments N by one. Flowreturns to decision block 2508.

Referring now to FIG. 26A, a program listing of an NNU program thatperforms a convolution of a data matrix 2406 with the convolution kernel2042 of FIG. 24 and writes it back to the weight RAM 124 is shown. Theprogram loops a number of times through a loop body of instructions ataddresses 1 through 9. An initialize NPU instruction at address 0specifies the number of times each NPU 126 executes the loop body, whichin the example of FIG. 26A has a loop count value of 400, correspondingto the number of rows in a data matrix 2406 of FIG. 24, and a loopinstruction at the end of the loop (at address 10) decrements thecurrent loop count value and if the result is non-zero causes control toreturn to the top of the loop body (i.e., to the instruction at address1). The initialize NPU instruction also clears the accumulator 202 tozero. Preferably, the loop instruction at address 10 also clears theaccumulator 202 to zero. Alternatively, as described above, themultiply-accumulate instruction at address 1 may specify to clear theaccumulator 202 to zero.

For each execution of the loop body of the program, the 512 NPUs 126concurrently perform 512 convolutions of the 3×3 convolution kernel 2402and 512 respective 3×3 sub-matrices of a data matrix 2406. Theconvolution is the sum of the nine products of an element of theconvolution kernel 2042 and its corresponding element of the respectivesub-matrix. In the embodiment of FIG. 26A, the origin (center element)of each of the 512 respective 3×3 sub-matrices is the data word Dx+1,y+1of FIG. 24, where y (column number) is the NPU 126 number, and x (rownumber) is the current weight RAM 124 row number that is read by themultiply-accumulate instruction at address 1 of the program of FIG. 26A(also, the row number is initialized by the initialize NPU instructionat address 0, incremented at each of the multiply-accumulateinstructions at addresses 3 and 5, and updated by the decrementinstruction at address 9). Thus, for each loop of the program, the 512NPUs 126 compute the 512 convolutions and write the 512 convolutionresults back to a specified row of the weight RAM 124. In the presentdescription, edge handling is ignored for simplicity, although it shouldbe noted that the use of the collective rotating feature of the NPUs 126will cause wrapping for two of the columns from one vertical edge of thedata matrix 2406 (e.g., of the image in the case of image processing) tothe other vertical edge (e.g., from the left edge to the right edge orvice versa). The loop body will now be described.

At address 1 is a multiply-accumulate instruction that specifies row 0of the data RAM 122 and implicitly uses the current weight RAM 124 row,which is preferably held in the sequencer 128 (and which is initializedto zero by the instruction at address 0 for the first pass through theloop body). That is, the instruction at address 1 causes each of theNPUs 126 to read its corresponding word from row 0 of the data RAM 122and read its corresponding word from the current weight RAM 124 row andperform a multiply-accumulate operation on the two words. Thus, forexample, NPU 5 multiplies C0,0 and Dx,5 (where “x” is the current weightRAM 124 row), adds the result to the accumulator 202 value 217 andwrites the sum back to the accumulator 202.

At address 2 is a multiply-accumulate instruction that specifies toincrement the data RAM 122 row (i.e., to row 1) and then read the rowfrom the data RAM 122 at the incremented address. The instruction alsospecifies to rotate the values in the mux-reg 705 of each NPU 126 to theadjacent NPU 126, which in this case is the row of data matrix 2406values just read from the weight RAM 124 in response to the instructionat address 1. In the embodiment of FIGS. 24 through 26, the NPUs 126 areconfigured to rotate the values of the mux-regs 705 to the left, i.e.,from NPU J to NPU J−1, rather than from NPU J to NPU J+1 as describedabove with respect to FIGS. 3, 7 and 19. It should be understood that inan embodiment in which the NPUs 126 are configured to rotate right, thearchitectural program may write the convolution kernel 2042 coefficientvalues to the data RAM 122 in a different order (e.g., rotated aroundits central column) in order to accomplish a similar convolution result.Furthermore, the architectural program may perform additionalpre-processing (e.g., transposition) of the convolution kernel 2042 asneeded. Additionally, the instruction specifies a count value of 2.Thus, the instruction at address 2 causes each of the NPUs 126 to readits corresponding word from row 1 of the data RAM 122 and receive therotated word into the mux-reg 705 and perform a multiply-accumulateoperation on the two words. Due to the count value of 2, the instructionalso causes each of the NPUs 126 to repeat the operation just described.That is, the sequencer 128 increments the data RAM 122 row address 123(i.e., to row 2) and each NPU 126 reads its corresponding word from row2 of the data RAM 122 and receives the rotated word into the mux-reg 705and performs a multiply-accumulate operation on the two words. Thus, forexample, assuming the current weight RAM 124 row is 27, after executingthe instruction at address 2, NPU 5 will have accumulated into itsaccumulator 202 the product of C0,1 and D27,6 and the product of C0,2and D27,7. Thus, after the completion of the instructions at addresses 1and 2, the product of C0,0 and D27,5, the product of C0,1 and D27,6, andthe product of C0,2 and D27,7 will have been accumulated into theaccumulator 202, along with all the other accumulated values fromprevious passes through the loop body.

The instructions at addresses 3 and 4 perform a similar operation as theinstructions at addresses 1 and 2, however for the next row of theweight RAM 124, by virtue of the weight RAM 124 row increment indicator,and for the next three rows of the data RAM 122, i.e., rows 3 through 5.That is, with respect to NPU 5, for example, after the completion of theinstructions at addresses 1 through 4, the product of C0,0 and D27,5,the product of C0,1 and D27,6, the product of C0,2 and D27,7, theproduct of C1,0 and D28,5, the product of C1,1 and D28,6, and theproduct of C1,2 and D28,7 will have been accumulated into theaccumulator 202, along with all the other accumulated values fromprevious passes through the loop body.

The instructions at addresses 5 and 6 perform a similar operation as theinstructions at addresses 3 and 4, however for the next row of theweight RAM 124, and for the next three rows of the data RAM 122, i.e.,rows 6 through 8. That is, with respect to NPU 5, for example, after thecompletion of the instructions at addresses 1 through 6, the product ofC0,0 and D27,5, the product of C0,1 and D27,6, the product of C0,2 andD27,7, the product of C1,0 and D28,5, the product of C1,1 and D28,6, theproduct of C1,2 and D28,7, the product of C2,0 and D29,5, the product ofC2,1 and D29,6, and the product of C2,2 and D29,7 will have beenaccumulated into the accumulator 202, along with all the otheraccumulated values from previous passes through the loop body. That is,after the completion of the instructions at addresses 1 through 6, andassuming the weight RAM 124 row at the beginning of the loop body was27, NPU 5, for example, will have used the convolution kernel 2042 toconvolve the following 3×3 sub-matrix:

D27,5 D27,6 D27,7 D28,5 D28,6 D28,7 D29,5 D29,6 D29,7

More generally, after the completion of the instructions at addresses 1through 6, each of the 512 NPUs 126 will have used the convolutionkernel 2042 to convolve the following 3×3 sub-matrix:

Dr, n Dr, n + 1 Dr, n + 2 Dr + 1, n Dr + 1, n + 1 Dr + 1, n + 2 Dr + 2,n Dr + 2, n + 1 Dr + 2, n + 2

where r is the weight RAM 124 row address value at the beginning of theloop body, and n is the NPU 126 number.

The instruction at address 7 passes through the accumulator 202 value217 through the AFU 212. The pass through function passes through a wordthat is the size (in bits) of the words read from the data RAM 122 andweight RAM 124 (i.e., in the example, 16 bits). Preferably, the user mayspecify the format of the output, e.g., how many of the output bits arefractional bits, as described in more detail below. Alternatively,rather than specifying a pass through activation function, a divideactivation function is specified that divides the accumulator 202 value217 by a divisor, such as described herein, e.g., with respect to FIGS.29A and 30, e.g., using one of the “dividers” 3014/3016 of FIG. 30. Forexample, in the case of a convolution kernel 2042 with a coefficient,such as the one-sixteenth coefficient of the Gaussian blur kerneldescribed above, rather than a pass through function, the activationfunction instruction at address 7 may specify a divide (e.g., by 16)activation function. Alternatively, the architectural program mayperform the divide by 16 on the convolution kernel 2042 coefficientsbefore writing them to the data RAM 122 and adjust the location of thebinary point accordingly for the convolution kernel 2402 values, e.g.,using the data binary point 2922 of FIG. 29, described below.

The instruction at address 8 writes the output of the AFU 212 to the rowof the weight RAM 124 specified by the current value of the output rowregister, which was initialized by the instruction at address 0 andwhich is incremented each pass through the loop by virtue of theincrement indicator in the instruction.

As may be determined from the example of FIGS. 24 through 26 having a3×3 convolution kernel 2402, the NPUs 126 read the weight RAM 124approximately every third clock cycle to read a row of the data matrix2406 and write the weight RAM 124 approximately every 12 clock cycles towrite the convolution result matrix. Additionally, assuming anembodiment that includes a write and read buffer such as the buffer 1704of FIG. 17, concurrently with the NPU 126 reads and writes, theprocessor 100 reads and writes the weight RAM 124 such that the buffer1704 performs one write and one read of the weight RAM 124 approximatelyevery 16 clock cycles to write the data matrices 2406 and to read theconvolution result matrices, respectively. Thus, approximately half thebandwidth of the weight RAM 124 is consumed by the hybrid manner inwhich the NNU 121 performs the convolution operation. Although theexample includes a 3×3 convolution kernel 2042, other size convolutionkernels may be employed, such as 2×2, 4×4, 5×5, 6×6, 7×7, 8×8, etc.matrices, in which case the NNU program will vary. In the case of alarger convolution kernel, a smaller percentage of the weight RAM 124bandwidth is consumed since the NPUs 126 read the weight RAM 124 asmaller percentage of the time because the count in the rotatingversions of the multiply-accumulate instructions is larger (e.g., ataddresses 2, 4 and 6 of the program of FIG. 26A and additional suchinstructions that would be needed for a larger convolution kernel).

Alternatively, rather than writing back the results of the convolutionsto different rows of the weight RAM 124 (e.g., 900-1299 and 1300-1699),the architectural program configures the NNU program to overwrite rowsof the input data matrix 2406 after the rows are no longer needed. Forexample, in the case of a 3×3 convolution kernel, rather than writingthe data matrix 2406 into rows 0-399 of the weight RAM 124, thearchitectural program writes the data matrix 2406 into rows 2-401, andthe NNU program is configured to write the convolution results to theweight RAM 124 beginning at row 0 and incrementing each pass through theloop body. In this fashion, the NNU program is overwriting only rowsthat are no longer needed. For example, after the first pass through theloop body (or more specifically after the execution of the instructionat address 1 which loads in row 0 of the weight RAM 124), the data inrow 0 can now be overwritten, although the data in rows 1-3 will beneeded in the second pass through the loop body and are therefore notoverwritten by the first pass through the loop body; similarly, afterthe second pass through the loop body, the data in row 1 can now beoverwritten, although the data in rows 2-4 will be needed in the secondpass through the loop body and are therefore not overwritten by thesecond pass through the loop body; and so forth. In such an embodiment,the height of each data matrix 2406 (chunk) may be larger (e.g., 800rows), resulting in fewer chunks.

Alternatively, rather than writing back the results of the convolutionsto the weight RAM 124, the architectural program configures the NNUprogram to write back the results of the convolutions to rows of thedata RAM 122 above the convolution kernel 2402 (e.g., above row 8), andthe architectural program reads the results from the data RAM 122 as theNNU 121 writes them (e.g., using the address of the most recentlywritten data RAM 122 row 2606 of FIG. 26B, described below). Thisalternative may be advantageous in an embodiment in which the weight RAM124 is single-ported and the data RAM 122 is dual-ported.

As may be observed from the operation of the NNU 121 according to theembodiment of FIGS. 24 through 26A, each execution of the program ofFIG. 26A takes approximately 5000 clock cycles and, consequently, theconvolving of the entire 2560×1600 data array 2404 of FIG. 24 takesapproximately 100,000 clock cycles, which may be considerably less thanthe number of clock cycles required to perform a similar task byconventional methods.

Referring now to FIG. 26B, a block diagram illustrating certain fieldsof the control register 127 of the NNU 121 of FIG. 1 according to oneembodiment is shown. The status register 127 includes a field 2602 thatindicates the address of the most recent row of the weight RAM 124written by the NPUs 126; a field 2606 that indicates the address of themost recent row of the data RAM 122 written by the NPUs 126; a field2604 that indicates the addresses of the most recent row of the weightRAM 124 read by the NPUs 126; and a field 2608 that indicates theaddresses of the most recent row of the data RAM 122 read by the NPUs126. This enables the architectural program executing on the processor100 to determine the progress of the NNU 121 as it marches throughreading and/or writing the data RAM 122 and/or weight RAM 124. Employingthis capability, along with the choice to overwrite the input datamatrix as described above (or to write the results to the data RAM 122,as mentioned above), the data array 2404 of FIG. 24 may be processed as5 chunks of 512×1600 rather than 20 chunks of 512×400, for example, asfollows. The processor 100 writes a first 512×1600 chunk into the weightRAM 124 starting at row 2 and starts the NNU program (which has a loopcount of 1600 and an initialized weight RAM 124 output row of 0). As theNNU 121 executes the NNU program, the processor 100 monitors thelocation/address of the weight RAM 124 output in order to (1) read(using MFNN 1500 instructions) the rows of the weight RAM 124 that havevalid convolution results written by the NNU 121 (beginning at row 0),and (2) to write the second 512×1600 data matrix 2406 (beginning at row2) over the valid convolution results once they have already been read,so that when the NNU 121 completes the NNU program on the first 512×1600chunk, the processor 100 can immediately update the NNU program asneeded and start it again to process the second 512×1600 chunk. Thisprocess is repeated three more times for the remaining three 512×1600chunks to accomplish high utilization of the NNU 121.

Advantageously, in one embodiment, the AFU 212 includes the ability toefficiently perform an effective division of the accumulator 202 value217, as described in more detail below, particularly with respect toFIGS. 29A and 29B and 30. For example, an activation function NNUinstruction that divides the accumulator 202 value 217 by 16 may be usedfor the Gaussian blurring matrix described above.

Although the convolution kernel 2402 used in the example of FIG. 24 is asmall static convolution kernel applied to the entire data array 2404,in other embodiments the convolution kernel may be a large matrix thathas unique weights associated with the different data values of the dataarray 2404, such as is commonly found in convolutional neural networks.When the NNU 121 is used in such a manner, the architectural program mayswap the locations of the data matrix and the convolution kernel, i.e.,place the data matrix in the data RAM 122 and the convolution kernel inthe weight RAM 124, and the number of rows that may be processed by agiven execution of the NNU program may be relatively smaller.

Referring now to FIG. 27, a block diagram illustrating an example of theweight RAM 124 of FIG. 1 populated with input data upon which a poolingoperation is performed by the NNU 121 of FIG. 1. A pooling operation,performed by a pooling layer of an artificial neural network, reducesthe dimensions of a matrix of input data (e.g., an image or convolvedimage) by taking sub-regions, or sub-matrices, of the input matrix andcomputing either the maximum or average value of the sub-matrices, andthe maximum or average values become a resulting matrix, or pooledmatrix. In the example of FIGS. 27 and 28, the pooling operationcomputes the maximum value of each sub-matrix. Pooling operations areparticularly useful in artificial neural networks that perform objectclassification or detection, for example. Generally, a pooling operationeffectively reduces the size of its input matrix by a factor of thenumber of elements in the sub-matrix examined, and in particular,reduces the input matrix in each dimension by the number of elements inthe corresponding dimension of the sub-matrix. In the example of FIG.27, the input data is a 512×1600 matrix of wide words (e.g., 16 bits)stored in rows 0 through 1599 of the weight RAM 124. In FIG. 27, thewords are denoted by their row, column location, e.g., the word in row 0and column 0 is denoted D0,0; the word in row 0 and column 1 is denotedD0,1; the word in row 0 and column 2 is denoted D0,2; and so forth tothe word in row 0 and column 511 is denoted D0,511. Similarly, the wordin row 1 and column 0 is denoted D1,0; the word in row 1 and column 1 isdenoted D1,1; the word in row 1 and column 2 is denoted D1,2; and soforth to the word in row 1 and column 511 is denoted D1,511; and soforth to the word in row 1599 and column 0 is denoted D1599,0; the wordin row 1599 and column 1 is denoted D1599,1; the word in row 1599 andcolumn 2 is denoted D1599,2; and so forth to the word in row 1599 andcolumn 511 is denoted D1599,511.

Referring now to FIG. 28, a program listing of an NNU program thatperforms a pooling operation of the input data matrix of FIG. 27 andwrites it back to the weight RAM 124 is shown. In the example of FIG.28, the pooling operation computes the maximum value of respective 4×4sub-matrices of the input data matrix. The program loops a number oftimes through a loop body of instructions at addresses 1 through 10. Aninitialize NPU instruction at address 0 specifies the number of timeseach NPU 126 executes the loop body, which in the example of FIG. 28 hasa loop count value of 400, and a loop instruction at the end of the loop(at address 11) decrements the current loop count value and if theresult is non-zero causes control to return to the top of the loop body(i.e., to the instruction at address 1). The input data matrix in theweight RAM 124 is effectively treated by the NNU program as 400 mutuallyexclusive groups of four adjacent rows, namely rows 0-3, rows 4-7, rows8-11 and so forth to rows 1596-1599. Each group of four adjacent rowsincludes 128 4×4 sub-matrices, namely the 4×4 sub-matrices of elementsformed by the intersection of the four rows of a group and four adjacentcolumns, namely columns 0-3, 4-7, 8-11 and so forth to columns 508-511.Of the 512 NPUs 126, every fourth NPU 126 of the 512 NPUs 126 (i.e.,128) performs a pooling operation on a respective 4×4 sub-matrix, andthe other three-fourths of the NPUs 126 are unused. More specifically,NPUs 0, 4, 8, and so forth to NPU 508 each perform a pooling operationon their respective 4×4 sub-matrix whose left-most column numbercorresponds to the NPU number and whose lower row corresponds to thecurrent weight RAM 124 row value, which is initialized to zero by theinitialize instruction at address 0 and is incremented by four upon eachiteration of the loop body, as described in more detail below. The 400iterations of the loop body correspond to the number of groups of 4×4sub-matrices of the input data matrix of FIG. 27 (the 1600 rows of theinput data matrix divided by 4). The initialize NPU instruction alsoclears the accumulator 202 to zero. Preferably, the loop instruction ataddress 11 also clears the accumulator 202 to zero. Alternatively, themaxwacc instruction at address 1 specifies to clear the accumulator 202to zero.

For each iteration of the loop body of the program, the 128 used NPUs126 concurrently perform 128 pooling operations of the 128 respective4×4 sub-matrices of the current 4-row group of the input data matrix.More specifically, the pooling operation determines the maximum-valuedelement of the sixteen elements of the 4×4 sub-matrix. In the embodimentof FIG. 28, for each NPU y of the used 128 NPUs 126, the lower leftelement of the 4×4 sub-matrix is element Dx,y of FIG. 27, where x is thecurrent weight RAM 124 row number at the beginning of the loop body,which is read by the maxwacc instruction at address 1 of the program ofFIG. 28 (also, the row number is initialized by the initialize NPUinstruction at address 0, and incremented at each of the maxwaccinstructions at addresses 3, 5 and 7). Thus, for each loop of theprogram, the used 128 NPUs 126 write back to a specified row of theweight RAM 124 the corresponding maximum-valued element of therespective 128 4×4 sub-matrices of the current group of rows. The loopbody will now be described.

At address 1 is a maxwacc instruction that implicitly uses the currentweight RAM 124 row, which is preferably held in the sequencer 128 (andwhich is initialized to zero by the instruction at address 0 for thefirst pass through the loop body). The instruction at address 1 causeseach of the NPUs 126 to read its corresponding word from the current rowof the weight RAM 124, compare the word to the accumulator 202 value217, and store in the accumulator 202 the maximum of the two values.Thus, for example, NPU 8 determines the maximum value of the accumulator202 value 217 and data word Dx,8 (where “x” is the current weight RAM124 row) and writes the maximum value back to the accumulator 202.

At address 2 is a maxwacc instruction that specifies to rotate thevalues in the mux-reg 705 of each NPU 126 to the adjacent NPU 126, whichin this case is the row of input data matrix values just read from theweight RAM 124 in response to the instruction at address 1. In theembodiment of FIGS. 27 through 28, the NPUs 126 are configured to rotatethe values of the mux-regs 705 to the left, i.e., from NPU J to NPU J−1,as described above with respect to FIGS. 24 through 26. Additionally,the instruction specifies a count value of 3. Thus, the instruction ataddress 2 causes each of the NPUs 126 to receive the rotated word intothe mux-reg 705 and determine the maximum value of the rotated word andthe accumulator 202 value 217, and then to repeat this operation twomore times. That is, each NPU 126 receives the rotated word into themux-reg 705 and determines the maximum value of the rotated word and theaccumulator 202 value 217 three times. Thus, for example, assuming thecurrent weight RAM 124 row at the beginning of the loop body is 36,after executing the instruction at addresses 1 and 2, NPU 8, forexample, will have stored in its accumulator 202 the maximum value ofthe accumulator 202 at the beginning of the loop body and the fourweight RAM 124 words D36, 8 and D36,9 and D36,10 and D36,11.

The maxwacc instructions at addresses 3 and 4 perform a similaroperation as the instructions at addresses 1 and 2, however for the nextrow of the weight RAM 124, by virtue of the weight RAM 124 row incrementindicator. That is, assuming the current weight RAM 124 row at thebeginning of the loop body is 36, after the completion of theinstructions at addresses 1 through 4, NPU 8, for example, will havestored in its accumulator 202 the maximum value of the accumulator 202at the beginning of the loop body and the eight weight RAM 124 wordsD36,8 and D36,9 and D36,10 and D36,11 and D37,8 and D37,9 and D37,10 andD37,11.

The maxwacc instructions at addresses 5 through 8 perform a similaroperation as the instructions at addresses 3 and 4, however for the nexttwo rows of the weight RAM 124. That is, assuming the current weight RAM124 row at the beginning of the loop body is 36, after the completion ofthe instructions at addresses 1 through 8, NPU 8, for example, will havestored in its accumulator 202 the maximum value of the accumulator 202at the beginning of the loop body and the sixteen weight RAM 124 wordsD36,8 and D36,9 and D36,10 and D36,11 and D37,8 and D37,9 and D37,10 andD37,11 and D38,8 and D38,9 and D38,10 and D38,11 and D39,8 and D39,9 andD39,10 and D39,11. That is, after the completion of the instructions ataddresses 1 through 8, and assuming the weight RAM 124 row at thebeginning of the loop body was 36, NPU 8, for example, will havedetermined the maximum value of the following 4×4 sub-matrix:

D36,8 D36,9 D36,10 D36,11 D37,8 D37,9 D37,10 D37,11 D38,8 D38,9 D38,10D38,11 D39,8 D39,9 D39,10 D39,11

More generally, after the completion of the instructions at addresses 1through 8, each of the used 128 NPUs 126 will have determined themaximum value of the following 4×4 sub-matrix:

Dr, n Dr, n + 1 Dr, n + 2 Dr, n + 3 Dr + 1, n Dr + 1, n + 1 Dr + 1, n +2 Dr + 1, n + 3 Dr + 2, n Dr + 2, n + 1 Dr + 2, n + 2 Dr + 2, n + 3 Dr +3, n Dr + 3, n + 1 Dr + 3, n + 2 Dr + 3, n + 3

where r is the weight RAM 124 row address value at the beginning of theloop body, and n is the NPU 126 number.

The instruction at address 9 passes through the accumulator 202 value217 through the AFU 212. The pass through function passes through a wordthat is the size (in bits) of the words read from the weight RAM 124(i.e., in the example, 16 bits). Preferably, the user may specify theformat of the output, e.g., how many of the output bits are fractionalbits, as described in more detail below.

The instruction at address 10 writes the accumulator 202 value 217 tothe row of the weight RAM 124 specified by the current value of theoutput row register, which was initialized by the instruction at address0 and which is incremented each pass through the loop by virtue of theincrement indicator in the instruction. More specifically, theinstruction at address 10 writes a wide word (e.g., 16 bits) of theaccumulator 202 to the weight RAM 124. Preferably, the instructionwrites the 16 bits as specified by the output binary point 2916, asdescribe in more detail below with respect to FIGS. 29A and 29B below.

As may be observed, each row written to the weight RAM 124 by aniteration of the loop body includes holes that have invalid data. Thatis, the resulting 133 wide words 1 through 3, 5 through 7, 9 through 11and so forth to wide words 509 through 511 are invalid, or unused. Inone embodiment, the AFU 212 includes a mux that enables packing of theresults into adjacent words of a row buffer, such as the row buffer 1104of FIG. 11, for writing back to the output weight RAM 124 row.Preferably, the activation function instruction specifies the number ofwords in each hole, and the number of words in the hole is used tocontrol the mux to pack the results. In one embodiment, the number ofholes may be specified as values from 2 to 6 in order to pack the outputof pooling 3×3, 4×4, 5×5, 6×6 or 7×7 sub-matrices. Alternatively, thearchitectural program executing on the processor 100 reads the resultingsparse (i.e., including holes) result rows from the weight RAM 124 andperforms the packing function using other execution units 112, such as amedia unit using architectural pack instructions, e.g., x86 SSEinstructions. Advantageously, in a concurrent manner similar to thosedescribed above and exploiting the hybrid nature of the NNU 121, thearchitectural program executing on the processor 100 may read the statusregister 127 to monitor the most recently written row of the weight RAM124 (e.g., field 2602 of FIG. 26B) to read a resulting sparse row, packit, and write it back to the same row of the weight RAM 124 so that itis ready to be used as an input data matrix for a next layer of theneural network, such as a convolution layer or a classic neural networklayer (i.e., multiply-accumulate layer). Furthermore, although anembodiment is described that performs pooling operations on 4×4sub-matrices, the NNU program of FIG. 28 may be modified to performpooling operations on other size sub-matrices such as 3×3, 5×5, 6×6 or7×7 sub-matrices.

As may also be observed, the number of result rows written to the weightRAM 124 is one-fourth the number of rows of the input data matrix.Finally, in the example, the data RAM 122 is not used. However,alternatively, the data RAM 122 may be used rather than the weight RAM124 to perform a pooling operation.

In the example of FIGS. 27 and 28, the pooling operation computes themaximum value of the sub-region. However, the program of FIG. 28 may bemodified to compute the average value of the sub-region by, for example,replacing the maxwacc instructions with sumwacc instructions (sum theweight word with the accumulator 202 value 217) and changing theactivation function instruction at address 9 to divide (preferably viareciprocal multiply, as described below) the accumulated results by thenumber of elements of each sub-region, which is sixteen in the example.

As may be observed from the operation of the NNU 121 according to theembodiment of FIGS. 27 and 28, each execution of the program of FIG. 28takes approximately 6000 clock cycles to perform a pooling operation ofthe entire 512×1600 data matrix of FIG. 27, which may be considerablyless than the number of clock cycles required to perform a similar taskby conventional methods.

Alternatively, rather than writing back the results of the poolingoperation to the weight RAM 124, the architectural program configuresthe NNU program to write back the results to rows of the data RAM 122,and the architectural program reads the results from the data RAM 122 asthe NNU 121 writes them (e.g., using the address of the most recentlywritten data RAM 122 row 2606 of FIG. 26B). This alternative may beadvantageous in an embodiment in which the weight RAM 124 issingle-ported and the data RAM 122 is dual-ported.

Fixed-Point Arithmetic with User-Supplied Binary Points, Full PrecisionFixed-Point Accumulation, User-Specified Reciprocal Value, StochasticRounding of Accumulator Value, and Selectable Activation/OutputFunctions

Generally speaking, hardware units that perform arithmetic in digitalcomputing devices may be divided into what are commonly termed “integer”units and “floating-point” units, because they perform arithmeticoperations on integer and floating-point numbers, respectively. Afloating-point number has a magnitude (or mantissa) and an exponent, andtypically a sign. The exponent is an indication of the location of theradix point (typically binary point) with respect to the magnitude. Incontrast, an integer number has no exponent, but only a magnitude, andfrequently a sign. An advantage of a floating-point unit is that itenables a programmer to work with numbers that can take on differentvalues within on an enormously large range, and the hardware takes careof adjusting the exponent values of the numbers as needed without theprogrammer having to do so. For example, assume the two floating-pointnumbers 0.111×10²⁹ and 0.81×10³¹ are multiplied. (A decimal, or base 10,example is used here, although floating-point units most commonly workwith base 2 floating-point numbers.) The floating-point unitautomatically takes care of multiplying the mantissa, adding theexponents, and then normalizing the result back to a value of0.8991×10⁵⁹. For another example, assume the same two floating-pointnumbers are added. The floating-point unit automatically takes care ofaligning the binary points of the mantissas before adding them togenerate a resulting sum with a value of 0.81111×10³¹.

However, the complexity and consequent increase in size, powerconsumption and clocks per instruction and/or lengthened cycle timesassociated with floating-point units is well known. Indeed, for thisreason many devices (e.g., embedded processors, microcontrollers andrelatively low cost and/or low power microprocessors) do not include afloating-point unit. As may be observed from the example above, some ofthe complexities of floating-point units include logic that performsexponent calculations associated with floating-point addition andmultiplication/division (adders to add/subtract exponents of operands toproduce resulting exponent value for floating-pointmultiplication/division, subtracters to determine subtract exponents ofoperands to determine binary point alignment shift amounts forfloating-point addition), shifters that accomplish binary pointalignment of the mantissas for floating-point addition, shifters thatnormalize floating-point results. Additionally, flow proceeds to blockunits typically require logic to perform rounding of floating-pointresults, logic to convert between integer and floating-point formats orbetween different floating-point precision formats (e.g., extendedprecision, double precision, single precision, half precision), leadingzero and leading one detectors, and logic to deal with specialfloating-point numbers, such as denormal numbers, NANs and infinity.

Furthermore, there is the disadvantage of the significant complexity inverification of the correctness of a floating-point unit largely due tothe increased number space over which the design must be verified, whichmay lengthen the product development cycle and time to market. Stillfurther, as described above, floating-point arithmetic implies thestorage and use of separate mantissa and exponent fields for eachfloating-point number involved in the computation, which may increasethe amount of storage required and/or reduce precision given an equalamount of storage to store integer numbers. Many of these disadvantagesare avoided by the use of integer units that perform arithmeticoperations on integer numbers.

Frequently, programmers write programs that process fractional numbers,i.e., numbers that are not whole numbers. The programs may run onprocessors that do not have a floating-point unit or, if they do, theinteger instructions executed by the integer units of the processor maybe faster. To take advantage of potential performance advantagesassociated with integer units, the programmer employs what is commonlyknown as fixed-point arithmetic on fixed-point numbers. Such programsinclude instructions that execute on integer units to process integernumbers, or integer data. The software is aware that the data isfractional and includes instructions that perform operations on theinteger data to deal with the fact that the data is actually fractional,e.g., alignment shifts. Essentially, the fixed-point software manuallyperforms some or all of the functionality that a floating-point unitperforms.

As used in the present disclosure, a “fixed-point” number (or value oroperand or input or output) is a number whose bits of storage areunderstood to include bits that represent a fractional portion of thefixed-point number, referred to herein as “fractional bits.” The bits ofstorage of the fixed-point number are comprised in a memory or register,e.g., an 8-bit or 16-bit word in a memory or register. Furthermore, thebits of storage of the fixed-point number are all used to represent amagnitude, and in some cases a bit is used to represent a sign, but noneof the storage bits of the fixed-point number are used to represent anexponent of the number. Furthermore, the number of fractional bits, orbinary point location, of the fixed-point number is specified in storagethat is distinct from the storage bits of the fixed-point number andthat in a shared, or global, fashion indicates the number of fractionalbits, or binary point location, for a set of fixed-point numbers towhich the fixed-point number belongs, such as the set of input operands,accumulated values or output results of an array of processing units,for example.

Advantageously, embodiments are described herein in which the ALUs areinteger units, but the activation function units include fixed-pointarithmetic hardware assist, or acceleration. This enables the ALUportions to be smaller and faster, which facilitates having more ALUswithin a given space on the die. This implies more neurons per diespace, which is particularly advantageous in a neural network unit.

Furthermore advantageously, in contrast to floating-point numbers thatrequire exponent storage bits for each floating-point number,embodiments are described in which fixed-point numbers are representedwith an indication of the number of bits of storage that are fractionalbits for an entire set of numbers, however, the indication is located ina single, shared storage that globally indicates the number offractional bits for all the numbers of the entire set, e.g., a set ofinputs to a series of operations, a set of accumulated values of theseries, a set of outputs. Preferably, the user of the NNU is enabled tospecify the number of fractional storage bits for the set of numbers.Thus, it should be understood that although in many contexts (e.g.,common mathematics) the term “integer” refers to a signed whole number,i.e., a number not having a fractional portion, the term “integer” inthe present context may refer to numbers having a fractional portion.Furthermore, the term “integer” in the present context is intended todistinguish from floating-point numbers for whom a portion of the bitsof their individual storage are used to represent an exponent of thefloating-point number. Similarly, an integer arithmetic operation, suchas an integer multiply or add or compare performed by an integer unit,assumes the operands do not have an exponent and therefore the integerelements of the integer unit, e.g., integer multiplier, integer adder,integer comparator, do not include logic to deal with exponents, e.g.,do not shift mantissas to align binary points for addition or compareoperations, do not add exponents for multiply operations.

Additionally, embodiments are described herein that include a largehardware integer accumulator to accumulate a large series of integeroperations (e.g., on the order of 1000 multiply-accumulates) withoutloss of precision. This enables the NNU to avoid dealing withfloating-point numbers while at the same time retaining full precisionin the accumulated values without having to saturate them or incurinaccurate results due to overflows. Once the series of integeroperations has accumulated a result into the full precision accumulator,the fixed-point hardware assist performs the necessary scaling andsaturating to convert the full-precision accumulated value to an outputvalue using the user-specified indications of the number of fractionalbits of the accumulated value and the desired number of fractional bitsin the output value, as described in more detail below.

As described in more detail below, preferably the activation functionunits may selectively perform stochastic rounding on the accumulatorvalue when compressing it from its full precision form for use as aninput to an activation function or for being passed through. Finally,the NPUs may be selectively instructed to apply different activationfunctions and/or output a variety of different forms of the accumulatorvalue as dictated by the different needs of a given layer of a neuralnetwork.

Referring now to FIG. 29A, a block diagram illustrating an embodiment ofthe control register 127 of FIG. 1 is shown. The control register 127may include a plurality of control registers 127. The control register127 includes the following fields, as shown: configuration 2902, signeddata 2912, signed weight 2914, data binary point 2922, weight binarypoint 2924, ALU function 2926, round control 2932, activation function2934, reciprocal 2942, shift amount 2944, output RAM 2952, output binarypoint 2954, and output command 2956. The control register 127 values maybe written by both an MTNN instruction 1400 and an instruction of an NNUprogram, such as an initiate instruction.

The configuration 2902 value specifies whether the NNU 121 is in anarrow configuration, a wide configuration or a funnel configuration, asdescribed above. The configuration 2902 implies the size of the inputwords received from the data RAM 122 and the weight RAM 124. In thenarrow and funnel configurations, the size of the input words is narrow(e.g., 8 bits or 9 bits), whereas in the wide configuration, the size ofthe input words is wide (e.g., 12 bits or 16 bits). Furthermore, theconfiguration 2902 implies the size of the output result 133, which isthe same as the input word size.

The signed data value 2912, if true, indicates the data words receivedfrom the data RAM 122 are signed values, and if false, indicates theyare unsigned values. The signed weight value 2914, if true, indicatesthe weight words received from the weight RAM 124 are signed values, andif false, indicates they are unsigned values.

The data binary point 2922 value indicates the location of the binarypoint for the data words received from the data RAM 122. Preferably, thedata binary point 2922 value indicates the number of bit positions fromthe right for the location of the binary point. Stated alternatively,the data binary point 2922 indicates how many of the least significantbits of the data word are fractional bits, i.e., to the right of thebinary point. Similarly, the weight binary point 2924 value indicatesthe location of the binary point for the weight words received from theweight RAM 124. Preferably, when the ALU function 2926 is a multiply andaccumulate or output accumulator, then the NPU 126 determines the numberof bits to the right of the binary point for the value held in theaccumulator 202 as the sum of the data binary point 2922 and the weightbinary point 2924. Thus, for example, if the value of the data binarypoint 2922 is 5 and the value of the weight binary point 2924 is 3, thenthe value in the accumulator 202 has 8 bits to the right of the binarypoint. When the ALU function 2926 is a sum/maximum accumulator anddata/weight word or pass through data/weight word, the NPU 126determines the number of bits to the right of the binary point for thevalue held in the accumulator 202 as the data/weight binary point2922/2924, respectively. In an alternate embodiment, described belowwith respect to FIG. 29B, rather than specifying an individual databinary point 2922 and weight binary point 2924, a single accumulatorbinary point 2923 is specified.

The ALU function 2926 specifies the function performed by the ALU 204 ofthe NPU 126. As described above, the ALU functions 2926 may include, butare not limited to: multiply data word 209 and weight word 203 andaccumulate product with accumulator 202; sum accumulator 202 and weightword 203; sum accumulator 202 and the data word 209; maximum ofaccumulator 202 and data word 209; maximum of accumulator 202 and weightword 203; output accumulator 202; pass through data word 209; passthrough weight word 203; output zero. In one embodiment, the ALUfunction 2926 is specified by an NNU initiate instruction and used bythe ALU 204 in response to an execute instruction (not shown). In oneembodiment, the ALU function 2926 is specified by individual NNUinstructions, such as the multiply-accumulate and maxwacc instructionsdescribed above.

The round control 2932 specifies which form of rounding is to be used bythe rounder 3004 (of FIG. 30). In one embodiment, the rounding modesthat may be specified include, but are not limited to: no rounding,round to nearest, and stochastic rounding. Preferably, the processor 100includes a random bit source 3003 (of FIG. 30) that generates randombits 3005 that are sampled and used to perform the stochastic roundingto reduce the likelihood of a rounding bias. In one embodiment, when theround bit 3005 is one and the sticky bit is zero, the NPU 126 rounds upif the sampled random bit 3005 is true and does not round up if therandom bit 3005 is false. In one embodiment, the random bit source 3003generates the random bits 3005 based on a sampling of random electricalcharacteristics of the processor 100, such as thermal noise across asemiconductor diode or resistor, although other embodiments arecontemplated.

The activation function 2934 specifies the function applied to theaccumulator 202 value 217 to generate the output 133 of the NPU 126. Asdescribed above and below in more detail, the activation functions 2934include, but are not limited to: sigmoid; hyperbolic tangent; softplus;rectify; divide by specified power of two; multiply by a user-specifiedreciprocal value to accomplish an effective division; pass-through fullaccumulator; and pass-through the accumulator as a canonical size, whichis described in more detail below. In one embodiment, the activationfunction is specified by an NNU activation function instruction.Alternatively, the activation function is specified by the initiateinstruction and applied in response to an output instruction, e.g.,write AFU output instruction at address 4 of FIG. 4, in which embodimentthe activation function instruction at address 3 of FIG. 4 is subsumedby the output instruction.

The reciprocal 2942 value specifies a value that is multiplied by theaccumulator 202 value 217 to accomplish a divide of the accumulator 202value 217. That is, the user specifies the reciprocal 2942 value as thereciprocal of the actual desired divisor. This is useful, for example,in conjunction with convolution and pooling operations, as describedherein. Preferably, the user specifies the reciprocal 2942 value in twoparts, as described in more detail with respect to FIG. 29C below. Inone embodiment, the control register 127 includes a field (not shown)that enables the user to specify division by one of a plurality ofbuilt-in divisor values that are the size of commonly used convolutionkernels, e.g., 9, 25, 36 or 49. In such an embodiment, the AFU 212 maystore reciprocals of the built-in divisors for multiplication by theaccumulator 202 value 217.

The shift amount 2944 specifies a number of bits that a shifter of theAFU 212 shifts the accumulator 202 value 217 right to accomplish adivide by a power of two. This may also be useful in conjunction withconvolution kernels whose size is a power of two.

The output RAM 2952 value specifies which of the data RAM 122 and theweight RAM 124 is to receive the output result 133.

The output binary point 2954 value indicates the location of the binarypoint for the output result 133. Preferably, the output binary point2954 indicates the number of bit positions from the right for thelocation of the binary point for the output result 133. Statedalternatively, the output binary point 2954 indicates how many of theleast significant bits of the output result 133 are fractional bits,i.e., to the right of the binary point. The AFU 212 performs rounding,compression, saturation and size conversion based on the value of theoutput binary point 2954 (as well as, in most cases, based on the valueof the data binary point 2922, the weight binary point 2924, theactivation function 2934, and/or the configuration 2902).

The output command 2956 controls various aspects of the output result133. In one embodiment, the AFU 212 employs the notion of a canonicalsize, which is twice the size (in bits) of the width specified by theconfiguration 2902. Thus, for example, if the configuration 2902 impliesthe size of the input words received from the data RAM 122 and theweight RAM 124 are 8 bits, then the canonical size is 16 bits; foranother example, if the configuration 2902 implies the size of the inputwords received from the data RAM 122 and the weight RAM 124 are 16 bits,then the canonical size is 32 bits. As described herein, the size of theaccumulator 202 is large (e.g., the narrow accumulator 202B is 28 bitsand the wide accumulator 202A is 41 bits) in order to preserve fullprecision of the intermediate computations, e.g., 1024 and 512 NNUmultiply-accumulate instructions, respectively. Consequently, theaccumulator 202 value 217 is larger (in bits) than the canonical size,and the AFU 212 (e.g., CCS 3008 described below with respect to FIG.30), for most values of the activation function 2934 (except forpass-through full accumulator), compresses the accumulator 202 value 217down to a value that is the canonical size. A first predetermined valueof the output command 2956 instructs the AFU 212 to perform thespecified activation function 2934 to generate an internal result thatis the same size as the original input words, i.e., half the canonicalsize, and to output the internal result as the output result 133. Asecond predetermined value of the output command 2956 instructs the AFU212 to perform the specified activation function 2934 to generate aninternal result that is twice the size as the original input words,i.e., the canonical size, and to output the lower half of the internalresult as the output result 133; and a third predetermined value of theoutput command 2956 instructs the AFU 212 to output the upper half ofthe canonical size internal result as the output result 133. A fourthpredetermined value of the output command 2956 instructs the AFU 212 tooutput the raw least-significant word (whose width specified by theconfiguration 2902) of the accumulator 202 as the output result 133; afifth predetermined value instructs the AFU 212 to output the rawmiddle-significant word of the accumulator 202 as the output result 133;and a sixth predetermined value instructs the AFU 212 to output the rawmost-significant word of the accumulator 202 as the output result 133,as described above with respect to FIGS. 8 through 10. As describedabove, outputting the full accumulator 202 size or the canonical sizeinternal result may be advantageous, for example, for enabling otherexecution units 112 of the processor 100 to perform activationfunctions, such as the softmax activation function.

Although the fields of FIG. 29A (and FIGS. 29B and 29C) are described asresiding in the control register 127, in other embodiments one or moreof the fields may reside in other parts of the NNU 121. Preferably, manyof the fields are included in the NNU instructions themselves anddecoded by the sequencer 128 to generate to a micro-operation 3416 (ofFIG. 34) that controls the ALUs 204 and/or AFUs 212. Additionally, thefields may be included in a micro-operation 3414 (of FIG. 34) stored ina media register 118 that controls the ALUs 204 and/or AFUs 212. In suchembodiments, the use of the initialize NNU instruction is minimized, andin other embodiments the initialize NNU instruction is eliminated.

As described above, an NNU instruction is capable of specifying toperform ALU operations on memory operands (e.g., word from data RAM 122and/or weight RAM 124) or a rotated operand (e.g., from the mux-regs208/705). In one embodiment, an NNU instruction may also specify anoperand as a registered output of an activation function (e.g., theoutput of register 3038 of FIG. 30). Additionally, as described above,an NNU instruction is capable of specifying to increment a current rowaddress of the data RAM 122 or weight RAM 124. In one embodiment, theNNU instruction may specify an immediate signed integer delta value thatis added to the current row to accomplish incrementing or decrementingby a value other than one.

Referring now to FIG. 29B, a block diagram illustrating an embodiment ofthe control register 127 of FIG. 1 according to an alternate embodimentis shown. The control register 127 of FIG. 29B is similar to the controlregister 127 of FIG. 29A; however, the control register 127 of FIG. 29Bincludes an accumulator binary point 2923. The accumulator binary point2923 indicates the location of the binary point for the accumulator 202.Preferably, the accumulator binary point 2923 value indicates the numberof bit positions from the right for the location of the binary point.Stated alternatively, the accumulator binary point 2923 indicates howmany of the least significant bits of the accumulator 202 are fractionalbits, i.e., to the right of the binary point. In this embodiment, theaccumulator binary point 2923 is specified explicitly, rather than beingdetermined implicitly, as described above with respect to the embodimentof FIG. 29A.

Referring now to FIG. 29C, a block diagram illustrating an embodiment ofthe reciprocal 2942 of FIG. 29A stored as two parts according to oneembodiment is shown. A first part 2962 is a shift value that indicatesthe number of suppressed leading zeroes 2962 in the true reciprocalvalue that the user desires to be multiplied by the accumulator 202value 217. The number of leading zeroes is the number of consecutivezeroes immediately to the right of the binary point. The second part2694 is the leading zero-suppressed reciprocal 2964 value, i.e., thetrue reciprocal value with all leading zeroes removed. In oneembodiment, the number of suppressed leading zeroes 2962 is stored asfour bits and the leading zero-suppressed reciprocal 2964 value isstored as 8-bit unsigned value.

To illustrate by example, assume the user desires the accumulator 202value 217 to be multiplied by the reciprocal of 49. The binaryrepresentation of the reciprocal of 49 represented with 13 fractionalbits is 0.0000010100111, which has five leading zeroes. In this case,the user populates the number of suppressed leading zeroes 2962 with avalue of five, and populates the leading zero-suppressed reciprocal 2964with a value of 10100111. After the reciprocal multiplier “divider A”3014 (of FIG. 30) multiplies the accumulator 202 value 217 and theleading zero-suppressed reciprocal 2964 value, it right-shifts theresulting product by the number of suppressed leading zeroes 2962. Suchan embodiment may advantageously accomplish high precision with arelatively small number of bits used to represent the reciprocal 2942value.

Referring now to FIG. 30, a block diagram illustrating in more detail anembodiment of an AFU 212 of FIG. 2 is shown. The AFU 212 includes thecontrol register 127 of FIG. 1; a positive form converter (PFC) andoutput binary point aligner (OBPA) 3002 that receives the accumulator202 value 217; a rounder 3004 that receives the accumulator 202 value217 and indication of the number of bits shifted out by the OBPA 3002; arandom bit source 3003 that generates random bits 3005, as describedabove; a first mux 3006 that receives the output of the PFC and OBPA3002 and the output of the rounder 3004; a compressor to canonical size(CC S) and saturator 3008 that receives the output of the first mux3006; a bit selector and saturator 3012 that receives the output of theCCS and saturator 3008; a rectifier 3018 that receives the output of theCCS and saturator 3008; a reciprocal multiplier 3014 that receives theoutput of the CC S and saturator 3008; a right shifter 3016 thatreceives the output of the CCS and saturator 3008; a hyperbolic tangent(tanh) module 3022 that receives the output of the bit selector andsaturator 3012; a sigmoid module 3024 that receives the output of thebit selector and saturator 3012; a softplus module 3026 that receivesthe output of the bit selector and saturator 3012; a second mux 3032that receives the outputs of the tanh module 3022, the sigmoid module3024, the softplus module 3026, the rectifier 3108, the reciprocalmultiplier 3014, the right shifter 3016 and the passed-through canonicalsize output 3028 of the CCS and saturator 3008; a sign restorer 3034that receives the output of the second mux 3032; a size converter andsaturator 3036 that receives the output of the sign restorer 3034; athird mux 3037 that receives the output of the size converter andsaturator 3036 and the accumulator output 217; and an output register3038 that receives the output of the mux 3037 and whose output is theresult 133 of FIG. 1.

The PFC and OBPA 3002 receive the accumulator 202 value 217. Preferably,the accumulator 202 value 217 is a full precision value, as describedabove. That is, the accumulator 202 has a sufficient number of bits ofstorage to hold an accumulated value that is the sum, generated by theinteger adder 244, of a series of products generated by the integermultiplier 242 without discarding any of the bits of the individualproducts of the multiplier 242 or sums of the adder 244 so that there isno loss of precision. Preferably, the accumulator 202 has at least asufficient number of bits to hold the maximum number of accumulations ofthe products that an NNU 121 is programmable to perform. For example,referring to the program of FIG. 4 to illustrate, the maximum number ofproduct accumulations the NNU 121 is programmable to perform when in awide configuration is 512, and the accumulator 202 bit width is 41. Foranother example, referring to the program of FIG. 20 to illustrate, themaximum number of product accumulations the NNU 121 is programmable toperform when in a narrow configuration is 1024, and the accumulator 202bit width is 28. To generalize, the full precision accumulator 202includes at least Q bits, where Q is the sum of M and log₂P, where M isthe bit width of the integer product of the multiplier 242 (e.g., 16bits for a narrow multiplier 242, or 32 bits for a wide multiplier 242)and P is the maximum permissible number of the integer products that maybe accumulated into the accumulator 202. Preferably, the maximum numberof product accumulations is specified via a programming specification tothe programmer of the NNU 121. In one embodiment, the sequencer 128enforces a maximum value of the count of a multiply-accumulate NNUinstruction (e.g., the instruction at address 2 of FIG. 4), for example,of 511, with the assumption of one previous multiply-accumulateinstruction that loads the row of data/weight words 206/207 from thedata/weight RAM 122/124 (e.g., the instruction at address 1 of FIG. 4).

Advantageously, by including an accumulator 202 that has a large enoughbit width to accumulate a full precision value for the maximum number ofallowable accumulations, this simplifies the design of the ALU 204portion of the NPU 126. In particular, it alleviates the need for logicto saturate sums generated by the integer adder 244 that would overflowa smaller accumulator and that would need to keep track of the binarypoint location of the accumulator to determine whether an overflow hasoccurred to know whether a saturation was needed. To illustrate byexample a problem with a design that included a non-full precisionaccumulator and instead includes saturating logic to handle overflows ofthe non-full precision accumulator, assume the following.

-   -   (1) The range of the data word values is between 0 and 1 and all        the bits of storage are used to store fractional bits. The range        of the weight words is between −8 and +8 and all but three of        the bits of storage are used to store fractional bits. And, the        range of the accumulated values for input to a hyperbolic        tangent activation function is between −8 and +8 and all but        three of the bits of storage are used to store fractional bits.    -   (2) The bit width of the accumulator is non-full precision        (e.g., only the bit width of the products).    -   (3) The final accumulated value would be somewhere between −8        and +8 (e.g., +4.2), assuming the accumulator were full        precision; however, the products before a “point A” in the        series tend to be positive much more frequently, whereas the        products after point A tend to be negative much more frequently.        In such a situation, an inaccurate result (i.e., a result other        than +4.2) might be obtained. This is because at some point        before point A the accumulator may be saturated to the maximum        +8 value when it should have been a larger value, e.g., +8.2,        causing loss of the remaining +0.2. The accumulator could even        remain at the saturated value for more product accumulations        resulting in loss of even more positive value. Thus, the final        value of the accumulator could be a smaller number than it would        have been (i.e., less then +4.2) if the accumulator had a full        precision bit width.

The PFC 3002 converts the accumulator 202 value 217 to a positive form,if the value is negative, and generates an additional bit that indicateswhether the original value was positive or negative, which is passeddown the AFU 212 pipeline along with the value. Converting to a positiveform simplifies subsequent operations by the AFU 212. For example, itenables only positive values to be inputted to the tanh 3022 and sigmoid3024 modules, thus simplifying them. Additionally, it simplifies therounder 3004 and the saturator 3008.

The OBPA 3002 shifts, or scales, the positive-form value right to alignit with the output binary point 2954 specified in the control register127. Preferably, the OBPA 3002 calculates the shift amount as adifference that is the number of fractional bits of the output (e.g.,specified by the output binary point 2954) subtracted from the number offractional bits of the accumulator 202 value 217 (e.g., specified by theaccumulator binary point 2923 or the sum of the data binary point 2922and the weight binary point 2924). Thus, for example, if the accumulator202 binary point 2923 is 8 (as in the example above) and the outputbinary point 2954 is 3, then the OBPA 3002 shifts the positive-formvalue right 5 bits to generate a result provided to the mux 3006 and tothe rounder 3004.

The rounder 3004 rounds the accumulator 202 value 217. Preferably, therounder 3004 generates a rounded version of the positive-form valuegenerated by the PFC and OBPA 3002 and provides the rounded version tothe mux 3006. The rounder 3004 rounds according to the round control2932 described above, which may include stochastic rounding using therandom bit 3005, as described above and below. The mux 3006 selects oneof its inputs, i.e., either the positive-form value from the PFC andOBPA 3002 or the rounded version thereof from the rounder 3004, based onthe round control 2932 (which may include stochastic rounding, asdescribed herein) and provides the selected value to the CCS andsaturator 3008. Preferably, if the round control 2932 specifies norounding, then the mux 3006 selects the output of the PFC and OBPA 3002,and otherwise selects the output of the rounder 3004. Other embodimentsare contemplated in which the AFU 212 performs additional rounding. Forexample, in one embodiment, the bit selector 3012 rounds based on lostlow-order bits when it compresses the bits of the CCS and saturator 3008output (described below). For another example, in one embodiment, theproduct of the reciprocal multiplier 3014 (described below) is rounded.For yet another example, in one embodiment, the size converter 3036rounds when it converts to the proper output size (described below),which may involve losing low-order bits used in the roundingdetermination.

The CCS 3008 compresses the mux 3006 output value to the canonical size.Thus, for example, if the NPU 126 is in a narrow or funnel configuration2902, then the CCS 3008 compresses the 28-bit mux 3006 output value to16 bits; and if the NPU 126 is in a wide configuration 2902, then theCCS 3008 compresses the 41-bit mux 3006 output value to 32 bits.However, before compressing to the canonical size, if the pre-compressedvalue is greater than the maximum value expressible in the canonicalform, the saturator 3008 saturates the pre-compressed value to themaximum value expressible in the canonical form. For example, if any ofthe bits of the pre-compressed value left of the most-significantcanonical form bit has a 1 value, then the saturator 3008 saturates tothe maximum value (e.g., to all 1's).

Preferably, the tanh 3022, sigmoid 3024 and softplus 3026 modulescomprise lookup tables, e.g., programmable logic arrays (PLA), read-onlymemories (ROM), combinational logic gates, and so forth. In oneembodiment, in order to simplify and reduce the size of the modules3022/3024/3026, they are provided an input value that has 3.4 form,i.e., three whole bits and four fractional bits, i.e., the input valuehas four bits to the right of the binary point and three bits to theleft of the binary point. These values are chosen because at theextremes of the input value range (−8, +8) of the 3.4 form, the outputvalues asymptotically approach their minimum/maximum values. However,other embodiments are contemplated that place the binary point at adifferent location, e.g., in a 4.3 form or a 2.5 form. The bit selector3012 selects the bits of the CCS and saturator 3008 output that satisfythe 3.4 form criteria, which involves compression, i.e., some bits arelost, since the canonical form has a larger number of bits. However,prior to selecting/compressing the CCS and saturator 3008 output value,if the pre-compressed value is greater than the maximum valueexpressible in the 3.4 form, the saturator 3012 saturates thepre-compressed value to the maximum value expressible in the 3.4 form.For example, if any of the bits of the pre-compressed value left of themost-significant 3.4 form bit has a 1 value, then the saturator 3012saturates to the maximum value (e.g., to all 1's).

The tanh 3022, sigmoid 3024 and softplus 3026 modules perform theirrespective activation functions (described above) on the 3.4 form valueoutput by the CCS and saturator 3008 to generate a result. Preferably,the result of the tanh 3022 and sigmoid 3024 modules is a 7-bit resultin a 0.7 form, i.e., zero whole bits and seven fractional bits, i.e.,the input value has seven bits to the right of the binary point.Preferably, the result of the softplus module 3026 is a 7-bit result ina 3.4 form, e.g., in the same form as the input to the module 3026.Preferably, the outputs of the tanh 3022, sigmoid 3024 and softplus 3026modules are extended to canonical form (e.g., leading zeroes added asnecessary) and aligned to have the binary point specified by the outputbinary point 2954 value.

The rectifier 3018 generates a rectified version of the output value ofthe CCS and saturator 3008. That is, if the output value of the CCS andsaturator 3008 (its sign is piped down as describe above) is negative,the rectifier 3018 outputs a value of zero; otherwise, the rectifier3018 outputs its input value. Preferably, the output of the rectifier3018 is in canonical form and has the binary point specified by theoutput binary point 2954 value.

The reciprocal multiplier 3014 multiplies the output of the CCS andsaturator 3008 by the user-specified reciprocal value specified in thereciprocal value 2942 to generate its canonical size product, which iseffectively the quotient of the output of the CCS and saturator 3008 andthe divisor that is the reciprocal of the reciprocal 2942 value.Preferably, the output of the reciprocal multiplier 3014 is in canonicalform and has the binary point specified by the output binary point 2954value.

The right shifter 3016 shifts the output of the CCS and saturator 3008by the user-specified number of bits specified in the shift amount value2944 to generate its canonical size quotient. Preferably, the output ofthe right shifter 3016 is in canonical form and has the binary pointspecified by the output binary point 2954 value.

The mux 3032 selects the appropriate input specified by the activationfunction 2934 value and provides the selection to the sign restorer3034, which converts the positive form output of the mux 3032 to anegative form if the original accumulator 202 value 217 was a negativevalue, e.g., to two's-complement form.

The size converter 3036 converts the output of the sign restorer 3034 tothe proper size based on the value of the output command 2956, whichvalues are described above with respect to FIG. 29A. Preferably, theoutput of the sign restorer 3034 has a binary point specified by theoutput binary point 2954 value. Preferably, for the first predeterminedvalue of the output command 2956, the size converter 3036 discards thebits of the upper half of the sign restorer 3034 output. Furthermore, ifthe output of the sign restorer 3034 is positive and exceeds the maximumvalue expressible in the word size specified by the configuration 2902or is negative and is less than the minimum value expressible in theword size, the saturator 3036 saturates its output to the respectivemaximum/minimum value expressible in the word size. For the second andthird predetermined values, the size converter 3036 passes through thesign restorer 3034 output.

The mux 3037 selects either the size converter and saturator 3036 outputor the accumulator 202 output 217, based on the output command 2956, forprovision to the output register 3038. More specifically, for the firstand second predetermined values of the output command 2956, the mux 3037selects the lower word (whose size is specified by the configuration2902) of the output of the size converter and saturator 3036. For thethird predetermined value, the mux 3037 selects the upper word of theoutput of the size converter and saturator 3036. For the fourthpredetermined value, the mux 3037 selects the lower word of the rawaccumulator 202 value 217; for the fifth predetermined value, the mux3037 selects the middle word of the raw accumulator 202 value 217; andfor the sixth predetermined value, the mux 3037 selects the upper wordof the raw accumulator 202 value 217. As describe above, preferably theAFU 212 pads the upper bits of the upper word of the raw accumulator 202value 217 to zero.

Referring now to FIG. 31, an example of operation of the AFU 212 of FIG.30 is shown. As shown, the configuration 2902 is set to a narrowconfiguration of the NPUs 126. Additionally, the signed data 2912 andsigned weight 2914 values are true. Additionally, the data binary point2922 value indicates the binary point for the data RAM 122 words islocated such that there are 7 bits to the right of the binary point, andan example value of the first data word received by one of the NPUs 126is shown as 0.1001110. Still further, the weight binary point 2924 valueindicates the binary point for the weight RAM 124 words is located suchthat there are 3 bits to the right of the binary point, and an examplevalue of the first data word received by the one of the NPUs 126 isshown as 00001.010.

The 16-bit product (which is accumulated with the initial zero value ofthe accumulator 202) of the first data and weight words is shown as000000.1100001100. Because the data binary point 2912 is 7 and theweight binary point 2914 is 3, the implied accumulator 202 binary pointis located such that there are 10 bits to the right of the binary point.In the case of a narrow configuration, the accumulator 202 is 28 bitswide, in the example embodiment. In the example, a value 217 of000000000000000001.1101010100 of the accumulator 202 after all the ALUoperations (e.g., all 1024 multiply-accumulates of FIG. 20) areperformed is shown.

The output binary point 2954 value indicates the binary point for theoutput is located such that there are 7 bits to the right of the binarypoint. Therefore, after passing through the OBPA 3002 and CCS 3008, theaccumulator 202 value 217 is scaled, rounded and compressed to thecanonical form value of 000000001.1101011. In the example, the outputbinary point location indicates 7 fractional bits, and the accumulator202 binary point location indicates 10 fractional bits. Therefore, theOBPA 3002 calculates a difference of 3 and scales the accumulator 202value 217 by shifting it right 3 bits. This is indicated in FIG. 31 bythe loss of the 3 least significant bits (binary 100) of the accumulator202 value 217. Further in the example, the round control 2932 valueindicates to use stochastic rounding, and in the example it is assumedthat the sampled random bit 3005 is true. Consequently, the leastsignificant bit was rounded up because the round bit of the accumulator202 value 217 (most significant bit of the 3 bits shifted out by thescaling of the accumulator 202 value 217) was one and the sticky bit(Boolean OR of the 2 least significant bits of the 3 bits shifted out bythe scaling of the accumulator 202 value 217) was zero, according to thedescription above.

The activation function 2934 indicates to use a sigmoid function, in theexample. Consequently, the bit selector 3012 selects the bits of thecanonical form value such that the input to the sigmoid module 3024 hasthree whole bits and four fractional bits, as described above, i.e., avalue of 001.1101, as shown. The sigmoid module 3024 outputs a valuethat is put in canonical form as shown of 000000000.1101110.

The output command 2956 in the example specifies the first predeterminedvalue, i.e., to output the word size indicated by the configuration2902, which in this case is a narrow word (8 bits). Consequently, thesize converter 3036 converts the canonical sigmoid output value to an 8bit quantity having an implied binary point located such that 7 bits areto the right of the binary point, yielding an output value of 01101110,as shown.

Referring now to FIG. 32, a second example of operation of the AFU 212of FIG. 30 is shown. The example of FIG. 32 illustrates operation of theAFU 212 when the activation function 2934 indicates to pass-through theaccumulator 202 value 217 in the canonical size. As shown, theconfiguration 2902 is set to a narrow configuration of the NPUs 126.

In the example, the accumulator 202 is 28 bits wide, and the accumulator202 binary point is located such that there are 10 bits to the right ofthe binary point (either because the sum of the data binary point 2912and the weight binary point 2914 is 10 according to one embodiment, orthe accumulator binary point 2923 is explicitly specified as having avalue of 10 according to an alternate embodiment, as described above).In the example, FIG. 32 shows a value 217 of000001100000011011.1101111010 of the accumulator 202 after all the ALUoperations are performed.

In the example, the output binary point 2954 value indicates the binarypoint for the output is located such that there are 4 bits to the rightof the binary point. Therefore, after passing through the OBPA 3002 andCCS 3008, the accumulator 202 value 217 is saturated and compressed tothe canonical form value of 111111111111.1111, as shown, that isreceived by the mux 3032 as the canonical size pass-through value 3028.

In the example, two output commands 2956 are shown. The first outputcommand 2956 specifies the second predetermined value, i.e., to outputthe lower word of the canonical form size. Since the size indicated bythe configuration 2902 is a narrow word (8 bits), which implies acanonical size of 16 bits, the size converter 3036 selects the lower 8bits of the canonical size pass-through value 3028 to yield an 8 bitvalue of 11111111, as shown. The second output command 2956 specifiesthe third predetermined value, i.e., to output the upper word of thecanonical form size. Consequently, the size converter 3036 selects theupper 8 bits of the canonical size pass-through value 3028 to yield an 8bit value of 11111111, as shown.

Referring now to FIG. 33, a third example of operation of the AFU 212 ofFIG. 30 is shown. The example of FIG. 33 illustrates operation of theAFU 212 when the activation function 2934 indicates to pass-through thefull raw accumulator 202 value 217. As shown, the configuration 2902 isset to a wide configuration of the NPUs 126 (e.g., 16-bit input words).

In the example, the accumulator 202 is 41 bits wide, and the accumulator202 binary point is located such that there are 8 bits to the right ofthe binary point (either because the sum of the data binary point 2912and the weight binary point 2914 is 8 according to one embodiment, orthe accumulator binary point 2923 is explicitly specified as having avalue of 8 according to an alternate embodiment, as described above). Inthe example, FIG. 33 shows a value 217 of001000000000000000001100000011011.11011110 of the accumulator 202 afterall the ALU operations are performed.

In the example, three output commands 2956 are shown. The first outputcommand 2956 specifies the fourth predetermined value, i.e., to outputthe lower word of the raw accumulator 202 value; the second outputcommand 2956 specifies the fifth predetermined value, i.e., to outputthe middle word of the raw accumulator 202 value; and the third outputcommand 2956 specifies the sixth predetermined value, i.e., to outputthe upper word of the raw accumulator 202 value. Since the sizeindicated by the configuration 2902 is a wide word (16 bits), FIG. 33shows that in response to the first output command 2956, the mux 3037selects the 16-bit value of 0001101111011110; in response to the secondoutput command 2956, the mux 3037 selects the 16-bit value of0000000000011000; and in response to the third output command 2956, themux 3037 selects the 16-bit value of 0000000001000000.

As discussed above, advantageously the NNU 121 operates on integer datarather than floating-point data. This has the advantage of simplifyingeach NPU 126, or at least the ALU 204 portion. For example, the ALU 204need not include adders that would be needed in a floating-pointimplementation to add the exponents of the multiplicands for themultiplier 242. Similarly, the ALU 204 need not include shifters thatwould be needed in a floating-point implementation to align binarypoints of the addends for the adder 234. As one skilled in the art willappreciate, floating point units are generally very complex; thus, theseare only examples of simplifications to the ALU 204, and othersimplifications are enjoyed by the instant integer embodiments withhardware fixed-point assist that enable the user to specify the relevantbinary points. The fact that the ALUs 204 are integer units mayadvantageously result in a smaller (and faster) NPU 126 than afloating-point embodiment, which further advantageously facilitates theincorporation of a large array of NPUs 126 into the NNU 121. The AFU 212portion deals with scaling and saturating the accumulator 202 value 217based on the, preferably user-specified, number of fractional bitsdesired in the accumulated value and number of fractional bits desiredin the output value. Advantageously, any additional complexity andaccompanying increase in size, power consumption and/or time in thefixed-point hardware assist of the AFUs 212 may be amortized by sharingthe AFUs 212 among the ALU 204 portions, as described with respect tothe embodiment of FIG. 11, for example, since the number of AFUs 1112may be reduced in a shared embodiment.

Advantageously, embodiments described herein enjoy many of the benefitsassociated with reduced complexity of hardware integer arithmetic unitsover floating-point arithmetic units, while still providing arithmeticoperations on fractional numbers, i.e., numbers with a binary point. Anadvantage of floating-point arithmetic is that it accommodatesarithmetic operations on data whose individual values may be anywherewithin a very wide range of values (which is effectively limited only bythe size of the exponent range, which may be very large). That is, eachfloating-point number has its own potentially unique exponent value.However, embodiments are described here that recognize and takeadvantage of the fact that there are certain applications in which theinput data is highly parallelized and whose values are within arelatively narrow range such that the “exponent” for all theparallelized values can be the same. Therefore, the embodiments enablethe user to specify the binary point location once for all the inputvalues and/or accumulated values. Similarly, the embodiments enable theuser to specify the binary point location once for all the outputvalues, recognizing and taking advantage of similar rangecharacteristics of the parallelized outputs. An artificial neuralnetwork is an example of such an application, although the embodimentsmay be employed to perform computations for other applications. Byspecifying the binary point location for the inputs once, rather thanfor each individual input number, the embodiments provide more efficientuse of memory space (e.g., require less memory) over a floating-pointimplementation and/or provide an increase in precision for a similaramount of memory since the bits that would be used for an exponent in afloating-point implementation can be used to specify more precision inthe magnitude.

Further advantageously, the embodiments recognize the potential loss ofprecision that could be experienced during the accumulation of a largeseries of integer operations (e.g., overflow or loss of fractional bitsof lesser significance) and provide a solution, primarily in the form ofa sufficiently large accumulator to avoid loss of precision.

Direct Execution of NNU Micro-Operation

Referring now to FIG. 34, a block diagram illustrating the processor 100of FIG. 1 and in more detail portions of the NNU 121 of FIG. 1 is shown.The NNU 121 includes pipeline stages 3401 of the NPUs 126. The pipelinestages 3401, separated by staging registers, include combinatorial logicthat accomplish the operation of the NPUs 126 as described herein, suchas Boolean logic gates, multiplexers, adders, multipliers, comparators,and so forth. The pipeline stages 3401 receive a micro-operation 3418from a mux 3402. The micro-operation 3418 flows down the pipeline stages3401 and controls their combinatorial logic. The micro-operation 3418 isa collection of bits. Preferably the micro-operation 3418 includes thebits of the data RAM 122 memory address 123, the weight RAM 124 memoryaddress 125, the program memory 129 memory address 131, the mux-reg208/705 control signals 213/713, the mux 802 control signals 803, andmany of the fields of the control register 127 (e.g., of FIGS. 29Athrough 29C, for example, among others. In one embodiment, themicro-operation 3418 comprises approximately 120 bits. The mux 3402receives a micro-operation from three different sources and selects oneof them as the micro-operation 3418 for provision to the pipeline stages3401.

One micro-operation source to the mux 3402 is the sequencer 128 ofFIG. 1. The sequencer 128 decodes the NNU instructions received from theprogram memory 129 and in response generates a micro-operation 3416provided to a first input of the mux 3402.

A second micro-operation source to the mux 3402 is a decoder 3404 thatreceives microinstructions 105 from a reservation station 108 of FIG. 1,along with operands from the GPR 116 and media registers 118.Preferably, the microinstructions 105 are generated by the instructiontranslator 104 in response to translating MTNN instructions 1400 andMFNN instructions 1500, as described above. The microinstructions 105may include an immediate field that specifies a particular function(which was specified by an MTNN instruction 1400 or an MFNN instruction1500), such as starting and stopping execution of a program in theprogram memory 129, directly executing a micro-operation from the mediaregisters 118, or reading/writing a memory of the NNU 121, as describedabove. The decoder 3404 decodes the microinstructions 105 and inresponse generates a micro-operation 3412 provided to a second input ofthe mux 3402. Preferably, in response to some functions 1432/1532 of anMTNN/MFNN 1400/1500 instruction, it is not necessary for the decoder3404 to generate a micro-operation 3412 to send down the pipeline 3401,for example, writing to the control register 127, starting execution ofa program in the program memory 129, pausing the execution of a programin the program memory 129, waiting for completion of the execution of aprogram in the program memory 129, reading from the status register 127and resetting the NNU 121.

A third micro-operation source to the mux 3402 is the media registers118 themselves. Preferably, as described above with respect to FIG. 14,a MTNN instruction 1400 may specify a function that instructs the NNU121 to directly execute a micro-operation 3414 provided from the mediaregisters 118 to a third input of the mux 3402. The direct execution ofa micro-operation 3414 provided by the architectural media registers 118may be particularly useful for test, e.g., built-in self test (BIST),and debug of the NNU 121.

Preferably, the decoder 3404 generates a mode indicator 3422 thatcontrols the mux 3402 selection. When an MTNN instruction 1400 specifiesa function to start running a program from the program memory 129, thedecoder 3404 generates a mode indicator 3422 value that causes the mux3402 to select the micro-operation 3416 from the sequencer 128 untileither an error occurs or until the decoder 3404 encounters an MTNNinstruction 1400 that specifies a function to stop running a programfrom the program memory 129. When an MTNN instruction 1400 specifies afunction that instructs the NNU 121 to directly execute amicro-operation 3414 provided from a media register 118, the decoder3404 generates a mode indicator 3422 value that causes the mux 3402 toselect the micro-operation 3414 from the specified media register 118.Otherwise, the decoder 3404 generates a mode indicator 3422 value thatcauses the mux 3402 to select the micro-operation 3412 from the decoder3404.

Variable Rate Neural Network Unit

There may be situations in which the NNU 121 runs a program and thensits idle waiting for the processor 100 to do something it needs beforeit can run its next program. For example, assume a situation similar tothat described with respect to FIGS. 3 through 6A in which the NNU 121runs two or more successive instances of amultiply-accumulate-activation function program (which may also bereferred to as a feed forward neural network layer program). It may takethe processor 100 significantly longer to write 512 KB worth of weightvalues into the weight RAM 124 that will be used by the next run of theNNU program than it will take for the NNU 121 to run the program. Statedalternatively, the NNU 121 may run the program in a relatively shortamount of time and then sit idle while the processor 100 finisheswriting the next weight values into the weight RAM 124 for the next runof the program. This situation is visually illustrated in FIG. 36A,which is described in more detail below. In such situations, it may beadvantageous to run the NNU 121 at a slower rate and take longer toexecute the program and thereby spread out over more time the energyconsumption required for the NNU 121 to run the program, which may tendto keep the temperature of the NNU 121 lower and perhaps of theprocessor 100 in general. This situation is referred to as relaxed modeand is visually illustrated in FIG. 36B, which is described in moredetail below.

Referring now to FIG. 35, a block diagram illustrating a processor 100that includes a variable rate NNU 121 is shown. The processor 100 issimilar to the processor 100 of FIG. 1 in many respects andlike-numbered elements are similar. The processor 100 of FIG. 35 alsoincludes clock generation logic 3502 coupled to the functional units ofthe processor 100, namely, the instruction fetch unit 101, theinstruction cache 102, the instruction translator 104, the rename unit106, the reservation stations 108, the NNU 121, the other executionunits 112, the memory subsystem 114, the general purpose registers 116and the media registers 118. The clock generation logic 3502 includes aclock generator, such as a phase-locked loop (PLL), that generates aclock signal having a primary clock rate, or clock frequency. Forexample, the primary clock rate may be 1 GHz, 1.5 GHz, 2 GHz and soforth. The clock rate indicates the number of cycles, e.g., oscillationsbetween a high and low state, of the clock signal per second.Preferably, the clock signal has a balanced duty cycle, i.e., high halfthe cycle and low the other half of the cycle; alternatively, the clocksignal has an unbalanced duty cycle in which the clock signal is in thehigh state longer than it is in the low state, or vice versa.Preferably, the PLL is configurable to generate the primary clock signalat multiple clock rates. Preferably, the processor 100 includes a powermanagement module that automatically adjusts the primary clock ratebased on various factors including the dynamically detected operatingtemperature of the processor 100, utilization, and commands from systemsoftware (e.g., operating system, BIOS) indicating desired performanceand/or power savings indicators. In one embodiment, the power managementmodule includes microcode of the processor 100.

The clock generation logic 3502 also includes a clock distributionnetwork, or clock tree. The clock tree distributes the primary clocksignal to the functional units of the processor 100, which are indicatedin FIG. 35 as clock signal 3506-1 to the instruction fetch unit 101,clock signal 3506-2 to the instruction cache 102, clock signal 3506-10to the instruction translator 104, clock signal 3506-9 to the renameunit 106, clock signal 3506-8 to the reservation stations 108, clocksignal 3506-7 to the NNU 121, clock signal 3506-4 to the other executionunits 112, clock signal 3506-3 to the memory subsystem 114, clock signal3506-5 to the general purpose registers 116 and clock signal 3506-6 tothe media registers 118, and which are referred to collectively as clocksignals 3506. The clock tree includes nodes, or wires, that transmit theprimary clock signals 3506 to their respective functional units.Additionally, preferably the clock generation logic 3502 includes clockbuffers that re-generate the primary clock signal as needed to providecleaner clock signals and/or boost the voltage levels of the primaryclock signal, particularly for long nodes. Additionally, each functionalunit may also include its own sub-clock tree, as needed, thatre-generates and/or boosts the respective primary clock signal 3506 itreceives.

The NNU 121 includes clock reduction logic 3504 that receives a relaxindicator 3512 and that receives the primary clock signal 3506-7 and, inresponse, generates a secondary clock signal. The secondary clock signalhas a clock rate that is either the same clock rate as the primary clockrate or, when in relaxed mode, that is reduced relative to the primaryclock rate by an amount programmed into the relax indicator 3512, whichpotentially provides thermal benefits. The clock reduction logic 3504 issimilar in many respects to the clock generation logic 3502 in that itincludes a clock distribution network, or clock tree, that distributesthe secondary clock signal to various blocks of the NNU 121, which areindicated as clock signal 3508-1 to the array of NPUs 126, clock signal3508-2 to the sequencer 128 and clock signal 3508-3 to the interfacelogic 3514, and which are referred to collectively or individually assecondary clock signal 3508. Preferably, the NPUs 126 include aplurality of pipeline stages 3401, as described with respect to FIG. 34,that include pipeline staging registers that receive the secondary clocksignal 3508-1 from the clock reduction logic 3504.

The NNU 121 also includes interface logic 3514 that receives the primaryclock signal 3506-7 and secondary clock signal 3508-3. The interfacelogic 3514 is coupled between the lower portions of the front end of theprocessor 100 (e.g., the reservation stations 108, media registers 118,and general purpose registers 116) and the various blocks of the NNU121, namely the clock reduction logic 3504, the data RAM 122, the weightRAM 124, the program memory 129 and the sequencer 128. The interfacelogic 3514 includes a data RAM buffer 3522, a weight RAM buffer 3524,the decoder 3404 of FIG. 34 and the relax indicator 3512. The relaxindicator 3512 holds a value that specifies how much slower, if any, thearray of NPUs 126 will execute NNU program instructions. Preferably, therelax indicator 3512 specifies a divisor value, N, by which the clockreduction logic 3504 divides the primary clock signal 3506-7 to generatethe secondary clock signal 3508 such that the secondary clock signal3508 has a rate that is 1/N. Preferably, the value of N may beprogrammed to any one of a plurality of different predetermined valuesto cause the clock reduction logic 3504 to generate the secondary clocksignal 3508 at a corresponding plurality of different rates that areless than the primary clock rate.

In one embodiment, the clock reduction logic 3504 comprises a clockdivider circuit to divide the primary clock signal 3506-7 by the relaxindicator 3512 value. In one embodiment, the clock reduction logic 3504comprises clock gates (e.g., AND gates) that gate the primary clocksignal 3506-7 with an enable signal that is true once only every Ncycles of the primary clock signal 3506-7. For example, a circuit thatincludes a counter that counts up to N may be used to generate theenable signal. When accompanying logic detects the output of the countermatches N, the logic generates a true pulse on the secondary clocksignal 3508 and resets the counter. Preferably the relax indicator 3512value is programmable by an architectural instruction, such as an MTNN1400 instruction of FIG. 14. Preferably, the architectural programrunning on the processor 100 programs the relax value into the relaxindicator 3512 just prior to instructing the NNU 121 to start runningthe NNU program, as described in more detail with respect to FIG. 37.

The weight RAM buffer 3524 is coupled between the weight RAM 124 andmedia registers 118 for buffering transfers of data between them.Preferably, the weight RAM buffer 3524 is similar to one or more of theembodiments of the buffer 1704 of FIG. 17. Preferably, the portion ofthe weight RAM buffer 3524 that receives data from the media registers118 is clocked by the primary clock signal 3506-7 at the primary clockrate and the portion of the weight RAM buffer 3524 that receives datafrom the weight RAM 124 is clocked by the secondary clock signal 3508-3at the secondary clock rate, which may or may not be reduced relative tothe primary clock rate depending upon the value programmed into therelax indicator 3512, i.e., depending upon whether the NNU 121 isoperating in relaxed or normal mode. In one embodiment, the weight RAM124 is single-ported, as described above with respect to FIG. 17, and isaccessible both by the media registers 118 via the weight RAM buffer3524 and by the NPUs 126 or the row buffer 1104 of FIG. 11 in anarbitrated fashion. In an alternate embodiment, the weight RAM 124 isdual-ported, as described above with respect to FIG. 16, and each portis accessible both by the media registers 118 via the weight RAM buffer3524 and by the NPUs 126 or the row buffer 1104 in a concurrent fashion.

Similarly, the data RAM buffer 3522 is coupled between the data RAM 122and media registers 118 for buffering transfers of data between them.Preferably, the data RAM buffer 3522 is similar to one or more of theembodiments of the buffer 1704 of FIG. 17. Preferably, the portion ofthe data RAM buffer 3522 that receives data from the media registers 118is clocked by the primary clock signal 3506-7 at the primary clock rateand the portion of the data RAM buffer 3522 that receives data from thedata RAM 122 is clocked by the secondary clock signal 3508-3 at thesecondary clock rate, which may or may not be reduced relative to theprimary clock rate depending upon the value programmed into the relaxindicator 3512, i.e., depending upon whether the NNU 121 is operating inrelaxed or normal mode. In one embodiment, the data RAM 122 issingle-ported, as described above with respect to FIG. 17, and isaccessible both by the media registers 118 via the data RAM buffer 3522and by the NPUs 126 or the row buffer 1104 of FIG. 11 in an arbitratedfashion. In an alternate embodiment, the data RAM 122 is dual-ported, asdescribed above with respect to FIG. 16, and each port is accessibleboth by the media registers 118 via the data RAM buffer 3522 and by theNPUs 126 or the row buffer 1104 in a concurrent fashion.

Preferably, the interface logic 3514 includes the data RAM buffer 3522and weight RAM buffer 3524, regardless of whether the data RAM 122and/or weight RAM 124 are single-ported or dual-ported, in order toprovide synchronization between the primary clock domain and thesecondary clock domain. Preferably, each of the data RAM 122, weight RAM124 and program memory 129 comprises a static RAM (SRAM) that includes arespective read enable, write enable and memory select signal.

As described above, the NNU 121 is an execution unit of the processor100. An execution unit is a functional unit of a processor that executesmicroinstructions into which architectural instructions are translated,such as the microinstructions 105 into which the architecturalinstructions 103 of FIG. 1 are translated, or that executesarchitectural instructions 103 themselves. An execution unit receivesoperands from general purpose registers of the processor, such as GPRs116 and media registers 118. An execution unit generates results inresponse to executing microinstructions or architectural instructionsthat may be written to the general purpose registers. Examples of thearchitectural instructions 103 are the MTNN instruction 1400 and theMFNN instruction 1500 described with respect to FIGS. 14 and 15,respectively. The microinstructions implement the architecturalinstructions. More specifically, the collective execution by theexecution unit of the one or more microinstructions into which anarchitectural instruction is translated performs the operation specifiedby the architectural instruction on inputs specified by thearchitectural instruction to produce a result defined by thearchitectural instruction.

Referring now to FIG. 36A, a timing diagram illustrating an example ofoperation of the processor 100 with the NNU 121 operating in normalmode, i.e., at the primary clock rate, is shown. Time progresses fromleft to right in the timing diagram. The processor 100 is running anarchitectural program at the primary clock rate. More specifically, theprocessor 100 front end (e.g., instruction fetch unit 101, instructioncache 102, instruction translator 104, rename unit 106, reservationstations 108) fetches, decodes and issues architectural instructions tothe NNU 121 and other execution units 112 at the primary clock rate.

Initially, the architectural program executes an architecturalinstruction (e.g., MTNN instruction 1400) that the front end 100 issuesto the NNU 121 that instructs the NNU 121 to start running an NNUprogram in its program memory 129. Prior, the architectural programexecuted an architectural instruction to write the relax indicator 3512with a value that specifies the primary clock rate, i.e., to put the NNU121 in normal mode. More specifically, the value programmed into therelax indicator 3512 causes the clock reduction logic 3504 to generatethe secondary clock signal 3508 at the primary clock rate of the primaryclock signal 3506. Preferably, in this case clock buffers of the clockreduction logic 3504 simply boost the primary clock signal 3506.Additionally prior, the architectural program executed architecturalinstructions to write to the data RAM 122 and the weight RAM 124 and towrite the NNU program into the program memory 129. In response to thestart NNU program MTNN instruction 1400, the NNU 121 starts running theNNU program at the primary clock rate, since the relax indicator 3512was programmed with the primary rate value. After starting the NNU 121running, the architectural program continues executing architecturalinstructions at the primary clock rate, including and predominately MTNNinstructions 1400 to write and/or read the data RAM 122 and weight RAM124 in preparation for the next instance, or invocation or run, of anNNU program.

As shown in the example in FIG. 36A, the NNU 121 finishes running theNNU program in significantly less time (e.g., one-fourth the time) thanthe architectural program takes to finish writing/reading the data RAM122 and weight RAM 124. For example, the NNU 121 may take approximately1000 clock cycles to run the NNU program, whereas the architecturalprogram takes approximately 4000 clock cycles to run, both at theprimary clock rate. Consequently, the NNU 121 sits idle the remainder ofthe time, which is a significantly long time in the example, e.g.,approximately 3000 primary clock rate cycles. As shown in the example inFIG. 36A, this pattern continues another time, and may continue forseveral more times, depending upon the size and configuration of theneural network. Because the NNU 121 may be a relatively large andtransistor-dense functional unit of the processor 100, it may generate asignificant amount of heat, particularly when running at the primaryclock rate.

Referring now to FIG. 36B, a timing diagram illustrating an example ofoperation of the processor 100 with the NNU 121 operating in relaxedmode, i.e., at a rate that is less than the primary clock rate, isshown. The timing diagram of FIG. 36B is similar in many respects to thetiming diagram of FIG. 36A in that the processor 100 is running anarchitectural program at the primary clock rate. And it is assumed inthe example that the architectural program and the NNU program of FIG.36B are the same as those of FIG. 36A. However, prior to starting theNNU program, the architectural program executed an MTNN instruction 1400that programmed the relax indicator 3512 with a value that causes theclock reduction logic 3504 to generate the secondary clock signal 3508at a secondary clock rate that is less than the primary clock rate. Thatis, the architectural program puts the NNU 121 in relaxed mode in FIG.36B rather than in normal mode as in FIG. 36A. Consequently, the NPUs126 execute the NNU program at the secondary clock rate, which in therelaxed mode is less than the primary clock rate. In the example, assumethe relax indicator 3512 is programmed with a value that specifies thesecondary clock rate is one-fourth the primary clock rate. As a result,the NNU 121 takes approximately four times longer to run the NNU programin relaxed mode than it does to run the NNU program in normal mode, asmay be seen by comparing FIGS. 36A and 36B, making the amount of timethe NNU 121 is idle relatively short. Consequently, the energy used torun the NNU program is consumed by the NNU 121 in FIG. 36B over a periodthat is approximately four times longer than when the NNU 121 ran theprogram in normal mode in FIG. 36A. Accordingly, the NNU 121 generatesheat to run the NNU program at approximately one-fourth the rate in FIG.36B as in FIG. 36A, which may have thermal benefits as described herein.

Referring now to FIG. 37, a flowchart illustrating operation of theprocessor 100 of FIG. 35 is shown. The flowchart illustrates operationin many respects similar to the operation described above with respectto FIGS. 35, 36A and 36B. Flow begins at block 3702.

At block 3702, the processor 100 executes MTNN instructions 1400 towrite the weight RAM 124 with weights and to write the data RAM 122 withdata. Flow proceeds to block 3704.

At block 3704, the processor 100 executes an MTNN instruction 1400 toprogram the relax indicator 3512 with a value that specifies a lowerrate than the primary clock rate, i.e., to place the NNU 121 intorelaxed mode. Flow proceeds to block 3706.

At block 3706, the processor 100 executes an MTNN instruction 1400 toinstruct the NNU 121 to start running an NNU program, similar to themanner visualized in FIG. 36B. Flow proceeds to block 3708.

At block 3708, the NNU 121 begins to run the NNU program. In parallel,the processor 100 executes MTNN instructions 1400 to write the weightRAM 124 with new weights (and potentially the data RAM 122 with newdata) and/or executes MFNN instructions 1500 to read results from thedata RAM 122 (and potentially from the weight RAM 124). Flow proceeds toblock 3712.

At block 3712, the processor 100 executes a MFNN instruction 1500 (e.g.,read the status register 127) to detect that the NNU 121 is finishedrunning its program. Assuming the architectural program selected a goodvalue of the relax indicator 3512, it should take the NNU 121 about thesame amount of time to run the NNU program as it takes the processor 100to execute the portion of the architectural program that accesses theweight RAM 124 and/or data RAM 122, as visualized in FIG. 36B. Flowproceeds to block 3714.

At block 3714, the processor 100 executes an MTNN instruction 1400 toprogram the relax indicator 3512 with a value that specifies the primaryclock rate, i.e., to place the NNU 121 into normal mode. Flow proceedsto block 3716.

At block 3716, the processor 100 executes an MTNN instruction 1400 toinstruct the NNU 121 to start running an NNU program, similar to themanner visualized in FIG. 36A. Flow proceeds to block 3718.

At block 3718, the NNU 121 begins to run the NNU program in normal mode.Flow ends at block 3718.

As described above, running the NNU program in relaxed made spreads outthe time over which the NNU runs the program relative to the time overwhich the NNU runs the program in normal mode (i.e., at the primaryclock rate of the processor), which may provide thermal benefits. Morespecifically, the devices (e.g., transistors, capacitors, wires) willlikely operate at lower temperatures while the NNU runs the program inrelaxed mode because the NNU generates at a slower rate the heat that isdissipated by the NNU (e.g., the semiconductor devices, metal layers,underlying substrate) and surrounding package and cooling solution(e.g., heat sink, fan). This may also lower the temperature of thedevices in other portions of the processor die in general. The loweroperating temperature of the devices, in particular their junctiontemperatures, may have the benefit of less leakage current. Furthermore,since the amount of current drawn per unit time is less, the inductivenoise and IR drop noise may be reduced. Still further, the lowertemperature may have a positive effect on the negative-bias temperatureinstability (NBTI) and positive-bias temperature instability (PBTI) ofMOSFETs of the processor, thereby increasing the reliability and/orlifetime of the devices and consequently the processor part. The lowertemperature may also reduce Joule heating and electromigration in metallayers of the processor.

Communication Mechanism Between Architectural Program andNon-Architectural Program Regarding Shared Resources of NNU

As described above, for example with respect to FIGS. 24 through 28 and35 through 37, the data RAM 122 and weight RAM 124 are shared resources.Both the NPUs 126 and the front-end of the processor 100 share the dataRAM 122 and weight RAM 124. More specifically, both the NPUs 126 and thefront-end of the processor 100, e.g., the media registers 118, write andread the data RAM 122 and the weight RAM 124. Stated alternatively, thearchitectural program running on the processor 100 shares the data RAM122 and weight RAM 124 with the NNU program running on the NNU 121, andin some situations this requires the control of flow between thearchitectural program and the NNU program, as described above. Thisresource sharing is also true of the program memory 129 to some extentbecause the architectural program writes it and the sequencer 128 readsit. Embodiments are described above and below that provide a highperformance solution to control the flow of access to the sharedresources between the architectural program and the NNU program.

Embodiments are described in which the NNU programs are also referred toas non-architectural programs, the NNU instructions are also referred toas non-architectural instructions, and the NNU instruction set (alsoreferred to above as the NPU instruction set) is also referred to as thenon-architectural instruction set. The non-architectural instruction setis distinct from the architectural instruction set. In embodiments inwhich the processor 100 includes an instruction translator 104 thattranslates architectural instructions into microinstructions, thenon-architectural instruction set is also distinct from themicroinstruction set.

Referring now to FIG. 38, a block diagram illustrating the sequencer 128of the NNU 121 in more detail is shown. The sequencer 128 provides thememory address 131 to the program memory 129 to select anon-architectural instruction that is provided to the sequencer 128, asdescribed above. The memory address 131 is held in a program counter3802 of the sequencer 128 as shown in FIG. 38. The sequencer 128generally increments through sequential addresses of the program memory129 unless the sequencer 128 encounters a non-architectural controlinstruction, such as a loop or branch instruction, in which case thesequencer 128 updates the program counter 3802 to the target address ofthe control instruction, i.e., to the address of the non-architecturalinstruction at the target of the control instruction. Thus, the address131 held in the program counter 3802 specifies the address in theprogram memory 129 of the non-architectural instruction of thenon-architectural program currently being fetched for execution by theNPUs 126. Advantageously, the value of the program counter 3802 may beobtained by the architectural program via the NNU program counter field3912 of the status register 127, as described below with respect to FIG.39. This enables the architectural program to make decisions about whereto read/write data from/to the data RAM 122 and/or weight RAM 124 basedon the progress of the non-architectural program.

The sequencer 128 also includes a loop counter 3804 that is used inconjunction with a non-architectural loop instruction, such as the loopto 1 instruction at address 10 of FIG. 26A and the loop to 1 instructionat address 11 of FIG. 28, for examples. In the examples of FIGS. 26A and28, the loop counter 3804 is loaded with a value specified in thenon-architectural initialize instruction at address 0, e.g., with avalue of 400. Each time the sequencer 128 encounters the loopinstruction and jumps to the target instruction (e.g., themultiply-accumulate instruction at address 1 of FIG. 26A or the maxwaccinstruction at address 1 of FIG. 28), the sequencer 128 decrements theloop counter 3804. Once the loop counter 3804 reaches zero, thesequencer 128 proceeds to the next sequential non-architecturalinstruction. In an alternate embodiment, when a loop instruction isfirst encountered, the loop counter 3804 is loaded with a loop countvalue specified in the loop instruction, obviating the need forinitialization of the loop counter 3804 via a non-architecturalinitialize instruction. Thus, the value of the loop counter 3804indicates how many more times a loop body of the non-architecturalprogram will be executed. Advantageously, the value of the loop counter3804 may be obtained by the architectural program via the loop count3914 field of the status register 127, as described below with respectto FIG. 39. This enables the architectural program to make decisionsabout where to read/write data from/to the data RAM 122 and/or weightRAM 124 based on the progress of the non-architectural program. In oneembodiment, the sequencer 128 includes three additional loop counters toaccommodate nested loops in the non-architectural program, and thevalues of the other three loop counters are also readable via the statusregister 127. A bit in the loop instruction indicates which of the fourloop counters is used for the instant loop instruction.

The sequencer 128 also includes an iteration counter 3806. The iterationcounter 3806 is used in conjunction with non-architectural instructionssuch as the multiply-accumulate instruction at address 2 of FIGS. 4, 9,20 and 26A, and the maxwacc instruction at address 2 of FIG. 28, forexamples, which will be referred to hereafter as “execute” instructions.In the examples above, each of the execute instructions specifies aniteration count of 511, 511, 1023, 2, and 3, respectively. When thesequencer 128 encounters an execute instruction that specifies anon-zero iteration count, the sequencer 128 loads the iteration counter3806 with the specified value. Additionally, the sequencer 128 generatesan appropriate micro-operation 3418 to control the logic in the NPU 126pipeline stages 3401 of FIG. 34 for execution and decrements theiteration counter 3806. If the iteration counter 3806 is greater thanzero, the sequencer 128 again generates an appropriate micro-operation3418 to control the logic in the NPUs 126 and decrements the iterationcounter 3806. The sequencer 128 continues in this fashion until theiteration counter 3806 reaches zero. Thus, the value of the iterationcounter 3806 indicates how many more times the operation specified inthe non-architectural execute instruction (e.g., multiply-accumulate,maximum, sum of the accumulator and a data/weight word) will beperformed. Advantageously, the value of the iteration counter 3806 maybe obtained by the architectural program via the iteration count 3916field of the status register 127, as described below with respect toFIG. 39. This enables the architectural program to make decisions aboutwhere to read/write data from/to the data RAM 122 and/or weight RAM 124based on the progress of the non-architectural program.

Referring now to FIG. 39, a block diagram illustrating certain fields ofthe control and status register 127 of the NNU 121 is shown. The fieldsinclude the address of the most recently written weight RAM row 2602 bythe NPUs 126 executing the non-architectural program, the address of themost recently read weight RAM row 2604 by the NPUs 126 executing thenon-architectural program, the address of the most recently written dataRAM row 2606 by the NPUs 126 executing the non-architectural program,and the address of the most recently read data RAM row 2604 by the NPUs126 executing the non-architectural program, which are described abovewith respect to FIG. 26B. Additionally, the fields include an NNUprogram counter 3912, a loop count 3914 and a iteration count 3916. Asdescribed above, the status register 127 is readable by thearchitectural program into the media registers 118 and/or generalpurpose registers 116, e.g., by MFNN instructions 1500, including theNNU program counter 3912, loop count 3914 and iteration count 3916 fieldvalues. The program counter 3912 value reflects the value of the programcounter 3802 of FIG. 38. The loop count 3914 value reflects the value ofthe loop counter 3804. The iteration count 3916 value reflects the valueof the iteration counter 3806. In one embodiment, the sequencer 128updates the program counter 3912, loop count 3914 and iteration count3916 field values each time it modifies the program counter 3802, loopcounter 3804, or iteration counter 3806 so that the field values arecurrent when the architectural program reads them. In anotherembodiment, when the NNU 121 executes an architectural instruction thatreads the status register 127, the NNU 121 simply obtains the programcounter 3802, loop counter 3804, and iteration counter 3806 values andprovides them back to the architectural instruction (e.g., into a mediaregister 118 or general purpose register 116).

As may be observed from the forgoing, the values of the fields of thestatus register 127 of FIG. 39 may be characterized as information thatindicates progress made by the non-architectural program during itsexecution by the NNU 121. Specific aspects of the non-architecturalprogram's progress have been described above, such as the programcounter 3802 value, the loop counter 3804 value, the iteration counter3806 value, the weight RAM 124 address 125 most recently written/read2602/2604, and the data RAM 122 address 123 most recently written/read2606/2608. The architectural program executing on the processor 100 mayread the non-architectural program progress values of FIG. 39 from thestatus register 127 and use the information to make decisions, e.g., byarchitectural instructions, such as compare and branch instructions. Forexample, the architectural program decides which rows to write/readdata/weights into/from the data RAM 122 and/or weight RAM 124 to controlthe flow of data in and out of the data RAM 122 or weight RAM 124,particularly for large data sets and/or for overlapping executioninstances of different non-architectural programs. Examples of thedecisions made by the architectural program are described above andbelow.

For example, as described above with respect to FIG. 26A, thearchitectural program configures the non-architectural program to writeback the results of the convolutions to rows of the data RAM 122 abovethe convolution kernel 2402 (e.g., above row 8), and the architecturalprogram reads the results from the data RAM 122 as the NNU 121 writesthem by using the address of the most recently written data RAM 122 row2606.

For another example, as described above with respect to FIG. 26B, thearchitectural program uses the information from the status register 127fields of FIG. 38 to determine the progress of a non-architecturalprogram to perform a convolution of the data array 2404 of FIG. 24 in 5chunks of 512×1600. The architectural program writes a first 512×1600chunk of the 2560×1600 data array 2404 into the weight RAM 124 andstarts the non-architectural program, which has a loop count of 1600 andan initialized weight RAM 124 output row of 0. As the NNU 121 executesthe non-architectural program, the architectural program reads thestatus register 127 to determine the most recently written weight RAMrow 2602 so that it may read the valid convolution results written bythe non-architectural program and write the next 512×1600 chunk over thevalid convolution results after the architectural program has read them,so that when the NNU 121 completes the non-architectural program on thefirst 512×1600 chunk, the processor 100 can immediately update thenon-architectural program as needed and start it again to process thenext 512×1600 chunk.

For another example, assume the architectural program is having the NNU121 perform a series of classic neural networkmultiply-accumulate-activation function operations in which the weightsare stored in the weight RAM 124 and the results are written back to thedata RAM 122. In this case, once the non-architectural program has reada row of the weight RAM 124 it will not be reading it again. So, thearchitectural program may be configured to begin overwriting the weightsin the weight RAM 124 with new weights for a next execution instance ofa non-architectural program (e.g., for a next neural network layer) oncethe current weights have been read/used by the non-architecturalprogram. In this case, the architectural program reads the statusregister 127 to obtain the address of the most recently read weight ramrow 2604 to decide where it may write the new set of weights into theweight RAM 124.

For another example, assume the architectural program knows that thenon-architectural program includes an execute instruction with a largeiteration count, such as the non-architectural multiply-accumulateinstruction at address 2 of FIG. 20. In such cases, the architecturalprogram may need to know the iteration count 3916 in order to knowapproximately how many more clock cycles it will take to complete thenon-architectural instruction so that the architectural program candecide which of two or more actions to take. For example, thearchitectural program may relinquish control to another architecturalprogram, such as the operating system, if the time is long. Similarly,assume the architectural program knows that the non-architecturalprogram includes a loop body with a relatively large loop count, such asthe non-architectural program of FIG. 28. In such cases, thearchitectural program may need to know the loop count 3914 in order toknow approximately how many more clock cycles it will take to completethe non-architectural program so that the architectural program candecide which of two or more actions to take.

For another example, assume the architectural program is having the NNU121 perform a pooling operation similar to that described with respectto FIGS. 27 and 28 in which the data to be pooled is stored in theweight RAM 124 and the results are written back to the weight RAM 124.However, assume that, unlike the example of FIGS. 27 and 28, the resultsare written back to the top 400 rows of the weight RAM 124, e.g., rows1600 to 1999. In this case, once the non-architectural program has readfour rows of the weight RAM 124 that it pools, it will not be reading itagain. So, the architectural program may be configured to beginoverwriting the data in the weight RAM 124 with new data (e.g., weightsfor a next execution instance of a non-architectural program, e.g., toperform classic multiply-accumulate-activation function operations onthe pooled data) once the current four rows have been read/used by thenon-architectural program. In this case, the architectural program readsthe status register 127 to obtain the address of the most recently readweight ram row 2604 to decide where it may write the new set of weightsinto the weight RAM 124.

Recurrent Neural Network Acceleration

A traditional feed-forward neural network includes no memory of previousinputs to the network. Feed-forward neural network are generally used toperform tasks in which the various inputs to the network over time areindependent of one another, as are the outputs. In contrast, recurrentneural networks (RNN) are generally helpful to perform tasks in whichthere is significance to the sequence of the inputs to the network overtime. (The sequence is commonly referred to as time steps.)Consequently, RNNs include a notion of memory, or internal state, thatholds information based on calculations made by the network in responseto previous inputs in the sequence, and the output of the RNN isdependent upon the internal state as well as the input of the next timestep. Speech recognition, language modeling, text generation, languagetranslation, image description generation, and certain forms ofhandwriting recognition are examples of tasks that tend to be performedwell by RNNs.

Three well-known examples are Elman RNNs, Jordan RNNs and Long ShortTerm Memory (LSTM) networks. An Elman RNN includes context nodes thatremember the state of a hidden layer of the RNN for a current time step,which is provided as an input to the hidden layer for the next timestep. Jordan RNNs are similar, except the context nodes remember thestate of the output layer of the RNN rather than the hidden layer. LSTMnetworks include an LSTM layer of LSTM cells. Each LSTM cell has acurrent state and a current output of a current time step and a newstate and a new output of a new, or next, time step. The LSTM cellincludes an input gate and an output gate, as well as a forget gate thatenables the cell to forget its remembered state. These three types ofRNNs are described in more detail below.

In the context of the present disclosure, with respect to a recurrentneural network (RNN) such as an Elman or Jordan RNN, the NNU performs atime step each instance in which it takes a set of input layer nodevalues and performs the computations necessary to propagate them throughthe RNN to generate the output layer node values, as well as the hiddenlayer and context layer node values. Thus, input layer node values areassociated with the time step in which they are used to compute hidden,output and context layer node values; and the hidden, output and contextlayer node values are associated with the time step in which they aregenerated. Input layer node values are sampled values of the systembeing modeled by the RNN, e.g., an image, a speech sample, a snapshot offinancial market data. With respect to an LSTM network, the NNU performsa time step each instance in which it takes a set of memory cell inputvalues and performs the computations necessary to generate the memorycell output values (as well as the cell state and input gate, forgetgate and output gate values), which may also be referred to aspropagating the cell input values through the LSTM layer cells. Thus,cell input values are associated with the time step in which they areused to compute the cell state and input gate, forget gate and outputgate values; and the cell state and input gate, forget gate and outputgate values are associated with the time step in which they aregenerated.

A context layer node value, also referred to as a state node, is stateof the neural network, and the state is based on the input layer nodevalues associated with previous time steps, not just the input layernode value associated with the current time step. The computationsperformed by the NNU for a time step (e.g., the hidden layer node valuecomputations for an Elman or Jordan RNN) are a function of the contextlayer node values generated in the previous time step. Therefore, thestate of the network (context node values) at the beginning of a timestep influences the output layer node values generated during the timestep. Furthermore, the state of the network at the end of the time stepis affected by both the input node values of the time step and the stateof the network at the beginning of the time step. Similarly, withrespect to an LSTM cell, a cell state value is based on the memory cellinput values associated with previous time steps, not just the memorycell input value associated with the current time step. Because thecomputations performed by the NNU for a time step (e.g., the next cellstate) are a function of the cell state values generated in the previoustime step, the state of the network (cell state values) at the beginningof the time step influences the cell output values generated during thetime step, and the state of the network at the end of the time step isaffected by both the cell input values of the time step and the previousstate of the network.

Referring now to FIG. 40, a block diagram illustrating an example of anElman RNN is shown. The Elman RNN of FIG. 40 includes input layer nodes,or neurons, denoted D0, D1 through Dn, referred to collectively as inputlayer nodes D and individually generically as input layer node D; hiddenlayer nodes/neurons denoted Z0, Z1 through Zn, referred to collectivelyas hidden layer nodes Z and individually generically as hidden layernode Z; output layer nodes/neurons denoted Y0, Y1 through Yn, referredto collectively as output layer nodes Y and individually generically asoutput layer node Y; and context layer nodes/neurons denoted C0, C1through Cn, referred to collectively as context layer nodes C andindividually generically as context layer node C. In the example ElmanRNN of FIG. 40, each of the hidden layer nodes Z has an input connectionwith the output of each of the input layer nodes D and has an inputconnection with the output of each of the context layer nodes C; each ofthe output layer nodes Y has an input connection with the output of eachof the hidden layer nodes Z; and each of the context layer nodes C hasan input connection with the output of a corresponding hidden layer nodeZ.

In many ways, the Elman RNN operates similarly to a traditionalfeed-forward artificial neural network. That is, for a given node, thereis a weight associated with each input connection to the node; the valuereceived by the node on an input connection is multiplied by itsassociated weight to generate a product; the node adds the productsassociated with all of the input connections to generate a sum (theremay also be a bias term included in the sum); typically, an activationfunction is performed on the sum to generate an output value of thenode, sometimes referred to as the node's activation. For a traditionalfeed forward network, the data always flow in one direction: from theinput layer to the output layer. That is, the input layer provides avalue to the hidden layer (typically multiple hidden layers), whichgenerates its output value that is provided to the output layer, whichgenerates an output that may be captured.

However, in contrast to a traditional feed-forward network, the ElmanRNN includes some connections that feed backward, namely the connectionsfrom the hidden layer nodes Z to the context layer nodes C of FIG. 40.The Elman RNN operates such that when the input layer nodes D provide aninput value to the hidden layer nodes Z in a new time step, the contextnodes C provide a value to the hidden layer Z that was the output valueof the hidden layer nodes Z in response to the previous input, referredto as the current time step. In this sense, the context nodes C of theElman RNN are a memory based on the input values of previous time steps.Operation of embodiments of the NNU 121 to perform computationsassociated with the Elman RNN of FIG. 40 will now be described withrespect to FIGS. 41 and 42.

For purposes of the present disclosure, an Elman RNN is a recurrentneural network comprising at least an input node layer, a hidden nodelayer, an output node layer, and a context node layer. For a given timestep, the context node layer stores results fed back by the hidden nodelayer to the context node layer that the hidden node layer generated inthe previous time step. The results fed back to the context layer may bethe results of an activation function or they may be results of theaccumulations performed by the hidden node layer without performance ofan activation function.

Referring now to FIG. 41, a block diagram illustrating an example of thelayout of data within the data RAM 122 and weight RAM 124 of the NNU 121as it performs calculations associated with the Elman RNN of FIG. 40 isshown. In the example of FIG. 41, the Elman RNN of FIG. 40 is assumed tohave 512 input nodes D, 512 hidden nodes Z, 512 context nodes C, and 512output nodes Y. Furthermore, it is assumed the Elman RNN is fullyconnected, i.e., all 512 input nodes D are connected as inputs to eachof the hidden nodes Z, all 512 context nodes C are connected as inputsto each of the hidden nodes Z, and all 512 hidden nodes Z are connectedas inputs to each of the output nodes Y. Additionally, the NNU 121 isconfigured as 512 NPUs 126, or neurons, e.g., in a wide configuration.Finally, it is assumed that the weights associated with the connectionsfrom the context nodes C to the hidden nodes Z all have a value of 1;consequently, there is no need to store these unitary weight values.

The lower 512 rows of the weight RAM 124 (rows 0 through 511) hold theweight values associated with the connections between the input nodes Dand the hidden nodes Z, as shown. More specifically, as shown, row 0holds the weights associated with the input connections to the hiddennodes Z from input node D0, i.e., word 0 holds the weight associatedwith the connection between input node D0 and hidden node Z0, word 1holds the weight associated with the connection between input node D0and hidden node Z1, word 2 holds the weight associated with theconnection between input node D0 and hidden node Z2, and so forth toword 511 holds the weight associated with the connection between inputnode D0 and hidden node Z511; row 1 holds the weights associated withthe input connections to the hidden nodes Z from input node D1, i.e.,word 0 holds the weight associated with the connection between inputnode D1 and hidden node Z0, word 1 holds the weight associated with theconnection between input node D1 and hidden node Z1, word 2 holds theweight associated with the connection between input node D1 and hiddennode Z2, and so forth to word 511 holds the weight associated with theconnection between input node D1 and hidden node Z511; through row 511holds the weights associated with the input connections to the hiddennodes Z from input node D511, i.e., word 0 holds the weight associatedwith the connection between input node D511 and hidden node Z0, word 1holds the weight associated with the connection between input node D511and hidden node Z1, word 2 holds the weight associated with theconnection between input node D511 and hidden node Z2, and so forth toword 511 holds the weight associated with the connection between inputnode D511 and hidden node Z511. This is similar to the layout and usedescribed above with respect to FIGS. 4 through 6A.

In a similar fashion, the next 512 rows of the weight RAM 124 (rows 512through 1023) hold the weight values associated with the connectionsbetween the hidden nodes Z and the output nodes Y, as shown.

The data RAM 122 holds the Elman RNN node values for a sequence of timesteps. More specifically, a triplet of three rows holds the node valuesfor a given time step. In an embodiment in which the data RAM 122 has 64rows, the data RAM 122 can hold the node values for 20 different timesteps, as shown. In the example of FIG. 41, rows 0 through 2 hold thenode values for time step 0, rows 3 through 5 hold the node values fortime step 1, and so forth to rows 57 through 59 hold the node values fortime step 19. The first row of a triplet holds the input node D valuesof the time step. The second row of a triplet holds the hidden node Zvalue of the time step. The third row of a triplet holds the output nodeY values of the time step. As shown, each column in the data RAM 122holds the node values for its corresponding neurons, or NPUs 126. Thatis, column 0 holds the node values associated with nodes D0, Z0 and Y0,whose computations are performed by NPU 0; column 1 holds the nodevalues associated with nodes D1, Z1 and Yl, whose computations areperformed by NPU 1; and so forth to column 511 holds the node valuesassociated with nodes D511, Z511 and Y511, whose computations areperformed by NPU 511, as described in more detail below with respect toFIG. 42.

As indicated in FIG. 41, the hidden node Z values in the second row of atriplet associated with a given time step are the context node C valuesfor the next time step. That is, the Z value that a NPU 126 computes andwrites during the time step becomes the C value used by the NPU 126(along with the next time step's input node D value) to compute the Zvalue during the next time step. The initial value of the context nodesC (i.e., the C value used to compute the Z value in row 1 for time step0) is assumed to be zero. This is described in more detail below withrespect to the non-architectural program of FIG. 42.

Preferably, the input node D values (in rows 0, 3, and so forth to 57 inthe example of FIG. 41) are written/populated in the data RAM 122 by thearchitectural program running on the processor 100 via MTNN instructions1400 and are read/used by the non-architectural program running on theNNU 121, such as the non-architectural program of FIG. 42. Conversely,the hidden/output node Z/Y values (in rows 1 and 2, 4 and 5, and soforth to 58 and 59 in the example of FIG. 41) are written/populated inthe data RAM 122 by the non-architectural program running on the NNU 121and are read/used by the architectural program running on the processor100 via MFNN instructions 1500. The example of FIG. 41 assumes thearchitectural program: (1) populates the data RAM 122 with the inputnode D values for 20 different time steps (rows 0, 3, and so forth to57); (2) starts the non-architectural program of FIG. 42; (3) detectsthe non-architectural program has completed; (4) reads out of the dataRAM 122 the output node Y values (rows 2, 5, and so forth to 59); and(5) repeats steps (1) through (4) as many times as needed to complete atask, e.g., computations used to perform the recognition of a statementmade by a user of a mobile phone.

In an alternative approach, the architectural program: (1) populates thedata RAM 122 with the input node D values for a single time step (e.g.,row 0); (2) starts the non-architectural program (a modified version ofFIG. 42 that does not require the loop and accesses a single triplet ofdata RAM 122 rows); (3) detects the non-architectural program hascompleted; (4) reads out of the data RAM 122 the output node Y values(e.g., row 2); and (5) repeats steps (1) through (4) as many times asneeded to complete a task. Either of the two approaches may bepreferable depending upon the manner in which the input values to theRNN are sampled. For example, if the task tolerates sampling the inputfor multiple time steps (e.g., on the order of 20) and performing thecomputations, then the first approach may be preferable since it islikely more computational resource efficient and/or higher performance,whereas, if the task cannot only tolerate sampling at a single timestep, the second approach may be required.

A third embodiment is contemplated that is similar to the secondapproach but in which, rather than using a single triplet of data RAM122 rows, the non-architectural program uses multiple triplets of rows,i.e., a different triplet for each time step, similar to the firstapproach. In the third embodiment, preferably the architectural programincludes a step prior to step (2) in which it updates thenon-architectural program before starting it, e.g., by updating the dataRAM 122 row in the instruction at address 1 to point to the nexttriplet.

Referring now to FIG. 42, a table illustrating a program for storage inthe program memory 129 of and execution by the NNU 121 to accomplish anElman RNN and using data and weights according to the arrangement ofFIG. 41 is shown. Some of the instructions of the non-architecturalprogram of FIG. 42 (and FIGS. 45, 48, 51, 54 and 57) have been describedin detail above (e.g., MULT-ACCUM, LOOP, INITIALIZE instructions), andthose descriptions are assumed in the following description unlessotherwise noted.

The example program of FIG. 42 includes 13 non-architecturalinstructions at addresses 0 through 12. The instruction at address 0(INITIALIZE NPU, LOOPCNT=20) clears the accumulator 202 and initializesthe loop counter 3804 to a value of 20 to cause the loop body (theinstructions of addresses 4 through 11) to be performed 20 times.Preferably, the initialize instruction also puts the NNU 121 in a wideconfiguration such that the NNU 121 is configured as 512 NPUs 126. Asmay be observed from the description below, the 512 NPUs 126 correspondto and operate as the 512 hidden layer nodes Z during the execution ofthe instructions of addresses 1 through 3 and 7 through 11, andcorrespond to and operate as the 512 output layer nodes Y during theexecution of the instructions of addresses 4 through 6.

The instructions at addresses 1 through 3 are outside the program loopbody and are executed only once. They compute an initial value of thehidden layer nodes Z and write them to row 1 of the data RAM 122 to beused by the first execution instance of the instructions at addresses 4through 6 to calculate the output layer nodes Y of the first time step(time step 0). Additionally, the hidden layer node Z values computed andwritten to row 1 of the data RAM 122 by the instructions at addresses 1through 3 become the context layer node C values to be used by the firstexecution instance of the instructions at addresses 7 and 8 in thecalculation of the hidden layer node Z values for the second time step(time step 1).

During the execution of the instructions at addresses 1 and 2, each NPU126 of the 512 NPUs 126 performs 512 multiply operations of the 512input node D values in row 0 of the data RAM 122 by the NPU's 126respective column of weights from rows 0 through 511 of the weight RAM124 to generate 512 products that are accumulated in the accumulator 202of the respective NPU 126. During execution of the instruction ataddress 3, the 512 accumulator 202 values of the 512 NPUs 126 are passedthrough and written to row 1 of the data RAM 122. That is, the outputinstruction of address 3 writes to row 1 of the data RAM 122 theaccumulator 202 value of each of the 512 NPUs 126, which is the initialhidden layer Z values, and then clears the accumulator 202.

The operations performed by the instructions at addresses 1 through 2 ofthe non-architectural program of FIG. 42 are in many ways similar to theoperations performed by the instructions at addresses 1 through 2 of thenon-architectural program of FIG. 4. More specifically, the instructionat address 1 (MULT-ACCUM DR ROW 0) instructs each of the 512 NPUs 126 toread into its mux-reg 208 the respective word of row 0 of the data RAM122, to read into its mux-reg 705 the respective word of row 0 of theweight RAM 124, to multiply the data word and the weight word togenerate a product and to add the product to the accumulator 202. Theinstruction at address 2 (MULT-ACCUM ROTATE, WR ROW+1, COUNT=511)instructs each of the 512 NPUs 126 to rotate into its mux-reg 208 theword from the adjacent NPU 126 (using the 512-word rotater formed by thecollective operation of the 512 mux-regs 208 of the NNU 121 into whichthe data RAM 122 row was just read by the instruction at address 1), toread into its mux-reg 705 the respective word of the next row of theweight RAM 124, to multiply the data word and the weight word togenerate a product and to add the product to the accumulator 202, and toperform this operation 511 times.

Furthermore, the single non-architectural output instruction of address3 of FIG. 42 (OUTPUT PASSTHRU, DR OUT ROW 1, CLR ACC) combines theoperations of the activation function instruction and the write outputinstruction of addresses 3 and 4 of FIG. 4 (although in the program ofFIG. 42 the accumulator 202 value is passed through whereas in theprogram of FIG. 4 an activation function is performed on the accumulator202 value). That is, in the program of FIG. 42, the activation function,if any, performed on the accumulator 202 value is specified in theoutput instruction (also in the output instructions of addresses 6 and11) rather than in a distinct non-architectural activation functioninstruction as in the program of FIG. 4. An alternate embodiment of thenon-architectural program of FIG. 4 (and FIGS. 20, 26A and 28) iscontemplated in which the operations of the activation functioninstruction and the write output instruction (e.g., of addresses 3 and 4of FIG. 4) are combined into a single non-architectural outputinstruction as in FIG. 42. The example of FIG. 42 assumes the nodes ofthe hidden layer (Z) perform no activation function on the accumulatorvalues. However, other embodiments are contemplated in which the hiddenlayer (Z) performs an activation function on the accumulator values, inwhich case the instructions at addresses 3 and 11 do so, e.g., sigmoid,tanh, rectify.

In contrast to the single execution instance of the instructions ataddresses 1 through 3, the instructions at addresses 4 through 11 areinside the program loop body and are executed the number of timesindicated in the loop count (e.g., 20). The first 19 execution instancesof the instructions at addresses 7 through 11 compute the value of thehidden layer nodes Z and write them to the data RAM 122 to be used bythe second through twentieth execution instances of the instructions ataddresses 4 through 6 to calculate the output layer nodes Y of theremaining time steps (time steps 1 through 19). (The last/twentiethexecution instance of the instructions at addresses 7 through 11computes the value of the hidden layer nodes Z and writes them to row 61of the data RAM 122, but they are not used.)

During the first execution instance of the instructions at addresses 4and 5 (MULT-ACCUM DR ROW+1, WR ROW 512 and MULT-ACCUM ROTATE, WR ROW+1,COUNT=511) (for time step 0), each NPU 126 of the 512 NPUs 126 performs512 multiply operations of the 512 hidden node Z values in row 1 of thedata RAM 122 (which were generated and written by the single executioninstance of the instructions of addresses 1 through 3) by the NPU's 126respective column of weights from rows 512 through 1023 of the weightRAM 124 to generate 512 products that are accumulated into theaccumulator 202 of the respective NPU 126. During the first executioninstance of the instruction at address 6 (OUTPUT ACTIVATION FUNCTION, DROUT ROW+1, CLR ACC), an activation function (e.g., sigmoid, tanh,rectify) is performed on the 512 accumulated values to compute theoutput node Y layer values and the results are written to row 2 of thedata RAM 122.

During the second execution instance of the instructions at addresses 4and 5 (for time step 1), each NPU 126 of the 512 NPUs 126 performs 512multiply operations of the 512 hidden node Z values in row 4 of the dataRAM 122 (which were generated and written by the first executioninstance of the instructions of addresses 7 through 11) by the NPU's 126respective column of weights from rows 512 through 1023 of the weightRAM 124 to generate 512 products that are accumulated into theaccumulator 202 of the respective NPU 126, and during the secondexecution instance of the instruction at address 6, the activationfunction is performed on the 512 accumulated values to compute theoutput node Y layer values that are written to row 5 of the data RAM122; during the third execution instance of the instructions ataddresses 4 and 5 (for time step 2), each NPU 126 of the 512 NPUs 126performs 512 multiply operations of the 512 hidden node Z values in row7 of the data RAM 122 (which were generated and written by the secondexecution instance of the instructions of addresses 7 through 11) by theNPU's 126 respective column of weights from rows 512 through 1023 of theweight RAM 124 to generate 512 products that are accumulated into theaccumulator 202 of the respective NPU 126, and during the thirdexecution instance of the instruction at address 6, the activationfunction is performed on the 512 accumulated values to compute theoutput node Y layer values and the results are written to row 8 of thedata RAM 122; and so forth until during the twentieth execution instanceof the instructions at addresses 4 and 5 (for time step 19), each NPU126 of the 512 NPUs 126 performs 512 multiply operations of the 512hidden node Z values in row 58 of the data RAM 122 (which were generatedand written by the nineteenth execution instance of the instructions ofaddresses 7 through 11) by the NPU's 126 respective column of weightsfrom rows 512 through 1023 of the weight RAM 124 to generate 512products that are accumulated into the accumulator 202 of the respectiveNPU 126, and during the twentieth execution instance of the instructionat address 6, the activation function is performed on the 512accumulated values to compute the output node Y layer values and theresults are written to row 59 of the data RAM 122.

During the first execution instance of the instructions at addresses 7and 8, each of the 512 NPUs 126 accumulates into its accumulator 202 the512 context node C values of row 1 of the data RAM 122 that weregenerated by the single execution instance of the instructions ofaddresses 1 through 3. More specifically, the instruction at address 7(ADD_D_ACC DR ROW+0) instructs each of the 512 NPUs 126 to read into itsmux-reg 208 the respective word of the current row of the data RAM 122(row 0 during the first execution instance) and add the word to theaccumulator 202. The instruction at address 8 (ADD_D_ACC ROTATE,COUNT=511) instructs each of the 512 NPUs 126 to rotate into its mux-reg208 the word from the adjacent NPU 126 (using the 512-word rotaterformed by the collective operation of the 512 mux-regs 208 of the NNU121 into which the data RAM 122 row was just read by the instruction ataddress 7) and add the word to the accumulator 202, and to perform thisoperation 511 times.

During the second execution instance of the instructions at addresses 7and 8, each of the 512 NPUs 126 accumulates into its accumulator 202 the512 context node C values of row 4 of the data RAM 122, which weregenerated and written by the first execution instance of theinstructions of addresses 9 through 11; during the third executioninstance of the instructions at addresses 7 and 8, each of the 512 NPUs126 accumulates into its accumulator 202 the 512 context node C valuesof row 7 of the data RAM 122, which were generated and written by thesecond execution instance of the instructions of addresses 9 through 11;and so forth until during the twentieth execution instance of theinstructions at addresses 7 and 8, each of the 512 NPUs 126 accumulatesinto its accumulator 202 the 512 context node C values of row 58 of thedata RAM 122, which were generated and written by the nineteenthexecution instance of the instructions of addresses 9 through 11.

As stated above, the example of FIG. 42 assumes the weights associatedwith the connections from the context nodes C to the hidden layer nodesZ all have a unitary value. However, in an alternate embodiment ElmanRNN in which these connections have non-zero weight values, the weightsare placed into the weight RAM 124 (e.g., in rows 1024 through 1535)prior to execution of the program of FIG. 42 and the program instructionat address 7 is MULT-ACCUM DR ROW+0, WR ROW 1024, and the programinstruction at address 8 is MULT-ACCUM ROTATE, WR ROW+1, COUNT=511.Preferably, the instruction at address 8 does not access the weight RAM124, but instead rotates the values read into the mux-regs 705 from theweight RAM 124 by the instruction at address 7. Not accessing the weightRAM 124 during the 511 clock cycles of the execution of the instructionat address 8 may be advantageous because it leaves more bandwidth forthe architectural program to access the weight RAM 124.

During the first execution instance of the instructions at addresses 9and 10 (MULT-ACCUM DR ROW+2, WR ROW 0 and MULT-ACCUM ROTATE, WR ROW+1,COUNT=511) (for time step 1), each NPU 126 of the 512 NPUs 126 performs512 multiply operations of the 512 input node D values in row 3 of thedata RAM 122 by the NPU's 126 respective column of weights from rows 0through 511 of the weight RAM 124 to generate 512 products that, alongwith the accumulation of the 512 context C node values performed by theinstructions at addresses 7 and 8, are accumulated into the accumulator202 of the respective NPU 126 to compute the hidden node Z layer values,and during the first execution of the instruction at address 11 (OUTPUTPASSTHRU, DR OUT ROW+2, CLR ACC), the 512 accumulator 202 values of the512 NPUs 126 are passed through and written to row 4 of the data RAM 122and the accumulator 202 is cleared; during the second execution instanceof the instructions at addresses 9 and 10 (for time step 2), each NPU126 of the 512 NPUs 126 performs 512 multiply operations of the 512input node D values in row 6 of the data RAM 122 by the NPU's 126respective column of weights from rows 0 through 511 of the weight RAM124 to generate 512 products that, along with the accumulation of the512 context C node values performed by the instructions at addresses 7and 8, are accumulated into the accumulator 202 of the respective NPU126 to compute the hidden node Z layer values, and during the secondexecution of the instruction at address 11, the 512 accumulator 202values of the 512 NPUs 126 are passed through and written to row 7 ofthe data RAM 122 and the accumulator 202 is cleared; and so forth untilduring the nineteenth execution instance of the instructions ataddresses 9 and 10 (for time step 19), each NPU 126 of the 512 NPUs 126performs 512 multiply operations of the 512 input node D values in row57 of the data RAM 122 by the NPU's 126 respective column of weightsfrom rows 0 through 511 of the weight RAM 124 to generate 512 productsthat, along with the accumulation of the 512 context C node valuesperformed by the instructions at addresses 7 and 8, are accumulated intothe accumulator 202 of the respective NPU 126 to compute the hidden nodeZ layer values, and during the nineteenth execution of the instructionat address 11, the 512 accumulator 202 values of the 512 NPUs 126 arepassed through and written to row 58 of the data RAM 122 and theaccumulator 202 is cleared. As alluded to above, the hidden node Z layervalues generated during the twentieth execution instance of theinstructions at addresses 9 and 10 and written to row 61 of the data RAM122 are not used.

The instruction at address 12 (LOOP 4) decrements the loop counter 3804and loops back to the instruction at address 4 if the new the loopcounter 3804 value is greater than zero.

Referring now to FIG. 43, a block diagram illustrating an example of aJordan RNN is shown. The Jordan RNN of FIG. 43 is similar in manyrespects to the Elman RNN of FIG. 40 in that it includes input layernodes/neurons D, hidden layer nodes/neurons Z, output layernodes/neurons Y, and context layer nodes/neurons C. However, in theJordan RNN of FIG. 43, the context layer nodes C have their inputconnections that feed backward from outputs of the corresponding outputlayer nodes Y, rather than from the outputs of the hidden layer nodes Zas in the Elman RNN of FIG. 40.

For purposes of the present disclosure, a Jordan RNN is a recurrentneural network comprising at least an input node layer, a hidden nodelayer, an output node layer, and a context node layer. At the beginningof a given time step, the context node layer contains results fed backby the output node layer to the context node layer that the output nodelayer generated in the previous time step. The results fed back to thecontext layer may be the results of an activation function or they maybe results of the accumulations performed by the output node layerwithout performance of an activation function.

Referring now to FIG. 44, a block diagram illustrating an example of thelayout of data within the data RAM 122 and weight RAM 124 of the NNU 121as it performs calculations associated with the Jordan RNN of FIG. 43 isshown. In the example of FIG. 44, the Jordan RNN of FIG. 43 is assumedto have 512 input nodes D, 512 hidden nodes Z, 512 context nodes C, and512 output nodes Y. Furthermore, it is assumed the Jordan RNN is fullyconnected, i.e., all 512 input nodes D are connected as inputs to eachof the hidden nodes Z, all 512 context nodes C are connected as inputsto each of the hidden nodes Z, and all 512 hidden nodes Z are connectedas inputs to each of the output nodes Y. In the example Jordan RNN ofFIG. 44, although an activation function is applied to the accumulator202 values to generate the output layer node Y values, it is assumedthat the accumulator 202 values prior to the application of theactivation function are passed through to the context layer nodes Crather than the actual output layer node Y values. Additionally, the NNU121 is configured as 512 NPUs 126, or neurons, e.g., in a wideconfiguration. Finally, it is assumed that the weights associated withthe connections from the context nodes C to the hidden nodes Z all havea value of 1; consequently, there is no need to store these unitaryweight values.

Like the example of FIG. 41, the lower 512 rows of the weight RAM 124(rows 0 through 511) hold the weight values associated with theconnections between the input nodes D and the hidden nodes Z, and thenext 512 rows of the weight RAM 124 (rows 512 through 1023) hold theweight values associated with the connections between the hidden nodes Zand the output nodes Y, as shown.

The data RAM 122 holds the Jordan RNN node values for a sequence of timesteps similar to the example of FIG. 41; however, a quadruplet of fourrows holds the node values for a given time step for the example of FIG.44. In an embodiment in which the data RAM 122 has 64 rows, the data RAM122 can hold the node values for 15 different time steps, as shown. Inthe example of FIG. 44, rows 0 through 3 hold the node values for timestep 0, rows 4 through 7 hold the node values for time step 1, and soforth to rows 60 through 63 hold the node values for time step 15. Thefirst row of a quadruplet holds the input node D values of the timestep. The second row of a quadruplet holds the hidden node Z value ofthe time step. The third row of a quadruplet holds the context node Cvalues of the time step. The fourth row of a quadruplet holds the outputnode Y values of the time step. As shown, each column in the data RAM122 holds the node values for its corresponding neurons, or NPUs 126.That is, column 0 holds the node values associated with nodes D0, Z0, C0and Y0, whose computations are performed by NPU 0; column 1 holds thenode values associated with nodes D1, Z1, C1 and Yl, whose computationsare performed by NPU 1; and so forth to column 511 holds the node valuesassociated with nodes D511, Z511, C511 and Y511, whose computations areperformed by NPU 511, as described in more detail below with respect toFIG. 44.

The context node C values shown in FIG. 44 for a given time step aregenerated in that time step and are used as inputs in the next timestep. That is, the C value that a NPU 126 computes and writes during thetime step becomes the C value used by the NPU 126 (along with the nexttime step's input node D value) to compute the Z value during the nexttime step. The initial value of the context nodes C (i.e., the C valueused to compute the Z value in row 1 for time step 0) is assumed to bezero. This is described in more detail below with respect to thenon-architectural program of FIG. 45.

As described above with respect to FIG. 41, preferably the input node Dvalues (in rows 0, 4, and so forth to 60 in the example of FIG. 44) arewritten/populated in the data RAM 122 by the architectural programrunning on the processor 100 via MTNN instructions 1400 and areread/used by the non-architectural program running on the NNU 121, suchas the non-architectural program of FIG. 45. Conversely, thehidden/context/output node Z/C/Y values (in rows 1/2/3, 4/5/6, and soforth to 60/61/62 in the example of FIG. 44) are written/populated inthe data RAM 122 by the non-architectural program running on the NNU 121and are read/used by the architectural program running on the processor100 via MFNN instructions 1500. The example of FIG. 44 assumes thearchitectural program: (1) populates the data RAM 122 with the inputnode D values for 15 different time steps (rows 0, 4, and so forth to60); (2) starts the non-architectural program of FIG. 45; (3) detectsthe non-architectural program has completed; (4) reads out of the dataRAM 122 the output node Y values (rows 3, 7, and so forth to 63); and(5) repeats steps (1) through (4) as many times as needed to complete atask, e.g., computations used to perform the recognition of a statementmade by a user of a mobile phone.

In an alternative approach, the architectural program: (1) populates thedata RAM 122 with the input node D values for a single time step (e.g.,row 0); (2) starts the non-architectural program (a modified version ofFIG. 45 that does not require the loop and accesses a single quadrupletof data RAM 122 rows); (3) detects the non-architectural program hascompleted; (4) reads out of the data RAM 122 the output node Y values(e.g., row 3); and (5) repeats steps (1) through (4) as many times asneeded to complete a task. Either of the two approaches may bepreferable depending upon the manner in which the input values to theRNN are sampled. For example, if the task tolerates sampling the inputfor multiple time steps (e.g., on the order of 15) and performing thecomputations, then the first approach may be preferable since it islikely more computational resource efficient and/or higher performance,whereas, if the task cannot only tolerate sampling at a single timestep, the second approach may be required.

A third embodiment is contemplated that is similar to the secondapproach but in which, rather than using a single quadruplet of data RAM122 rows, the non-architectural program uses multiple quadruplets ofrows, i.e., a different quadruplet for each time step, similar to thefirst approach. In the third embodiment, preferably the architecturalprogram includes a step prior to step (2) in which it updates thenon-architectural program before starting it, e.g., by updating the dataRAM 122 row in the instruction at address 1 to point to the nextquadruplet.

Referring now to FIG. 45, a table illustrating a program for storage inthe program memory 129 of and execution by the NNU 121 to accomplish aJordan RNN and using data and weights according to the arrangement ofFIG. 44 is shown. The non-architectural program of FIG. 45 is similar inmany respects to the non-architectural of FIG. 42, although differencesare described.

The example program of FIG. 45 includes 14 non-architecturalinstructions at addresses 0 through 13. The instruction at address 0 isan initialize instruction that clears the accumulator 202 andinitializes the loop counter 3804 to a value of 15 to cause the loopbody (the instructions of addresses 4 through 12) to be performed 15times. Preferably, the initialize instruction also puts the NNU 121 in awide configuration such that the NNU 121 is configured as 512 NPUs 126.As may be observed, the 512 NPUs 126 correspond to and operate as the512 hidden layer nodes Z during the execution of the instructions ofaddresses 1 through 3 and 8 through 12, and correspond to and operate asthe 512 output layer nodes Y during the execution of the instructions ofaddresses 4, 5 and 7.

The instructions at addresses 1 through 5 and 7 are the same as theinstructions at addresses 1 through 6 of FIG. 42 and perform the samefunctions. The instructions at addresses 1 through 3 compute an initialvalue of the hidden layer nodes Z and write them to row 1 of the dataRAM 122 to be used by the first execution instance of the instructionsat addresses 4, 5 and 7 to calculate the output layer nodes Y of thefirst time step (time step 0).

During the first execution instance of the output instruction at address6, the 512 accumulator 202 values accumulated by the instructions ataddresses 4 and 5 (which are subsequently used by the output instructionat address 7 to compute and write the output node Y layer values) arepassed through and written to row 2 of the data RAM 122, which are thecontext layer node C values produced in the first time step (time step0) and used during the second time step (time step 1); during the secondexecution instance of the output instruction at address 6, the 512accumulator 202 values accumulated by the instructions at addresses 4and 5 (which are subsequently used by the output instruction at address7 to compute and write the output node Y layer values) are passedthrough and written to row 6 of the data RAM 122, which are the contextlayer node C values produced in the second time step (time step 1) andused during the third time step (time step 2); and so forth until duringthe fifteenth execution instance of the output instruction at address 6,the 512 accumulator 202 values accumulated by the instructions ataddresses 4 and 5 (which are subsequently used by the output instructionat address 7 to compute and write the output node Y layer values) arepassed through and written to row 58 of the data RAM 122, which are thecontext layer node C values produced in the fifteenth time step (timestep 14) (and which are read by the instruction at address 8, but theyare not used).

The instructions at addresses 8 through 12 are the same as theinstructions at addresses 7 through 11 of FIG. 42, with one difference,and perform the same functions. The difference is the instruction ataddress 8 of FIG. 45 the data RAM 122 row is incremented by one(ADD_D_ACC DR ROW+1), whereas in the instruction at address 7 of FIG. 42the data RAM 122 row is incremented by zero (ADD_D_ACC DR ROW+0). Thisis due to the difference in layout of the data in the data RAM 122,specifically, that the layout in FIG. 44 includes a separate row in thequadruplet for the context layer node C values (e.g., rows 2, 6, 10,etc.) whereas the layout in FIG. 41 does not include a separate row inthe triplet for the context layer node C values but instead the contextlayer node C values share a row with the hidden layer node Z values(e.g., rows 1, 4, 7, etc.). The 15 execution instances of theinstructions at addresses 8 through 12 compute the value of the hiddenlayer nodes Z and write them to the data RAM 122 (at rows 5, 9, 13 andso forth to 57) to be used by the second through sixteenth executioninstances of the instructions at addresses 4, 5 and 7 to calculate theoutput layer nodes Y of the second through fifteenth time steps (timesteps 1 through 14). (The last/fifteenth execution instance of theinstructions at addresses 8 through 12 computes the value of the hiddenlayer nodes Z and writes them to row 61 of the data RAM 122, but theyare not used.)

The loop instruction at address 13 decrements the loop counter 3804 andloops back to the instruction at address 4 if the new the loop counter3804 value is greater than zero.

In an alternate embodiment, the Jordan RNN is designed such that thecontext nodes C hold the activation function values of the output nodesY, i.e., the accumulated values upon which the activation function hasbeen performed. In such an embodiment, the non-architectural instructionat address 6 is not included in the non-architectural program since thevalues of the output nodes Y are the same as the values of the contextnodes C. Hence, fewer rows of the data RAM 122 are consumed. To be moreprecise, each of the rows of FIG. 44 that hold context node C values(e.g., 2, 6, 59) are not present. Additionally, each time step requiresonly three rows of the data RAM 122, such that 20 time steps areaccommodated, rather than 15, and the addressing of the instructions ofthe non-architectural program of FIG. 45 is modified appropriately.

LSTM Cells

The notion of a Long Short Term Memory (LSTM) cell for use in recurrentneural networks has been long known. See, for example, Long Short-TermMemory, Sepp Hochreiter and Jürgen Schmidhuber, Neural Computation, Nov.15, 1997, Vol. 9, No. 8, Pages 1735-1780; Learning to Forget: ContinualPrediction with LSTM, Felix A. Gers, Jürgen Schmidhuber, and FredCummins, Neural Computation, October 2000, Vol. 12, No. 10, Pages2451-2471; both available from MIT Press Journals. LSTM cells may beconstructed in various forms. The LSTM cell 4600 described below withrespect to FIG. 46 is modeled after the LSTM cell described in thetutorial found at http://deeplearning.net/tutorial/lstm.html entitledLSTM Networks for Sentiment Analysis, a copy of which was downloaded onOct. 19, 2015 (hereafter “the LSTM tutorial”) and is provided in anInformation Disclosure Statement (IDS) provided herewith. The LSTM cell4600 is provided as a means to illustrate the ability of embodiments ofthe NNU 121 described herein to efficiently perform computationsassociated with LSTMs generally. It should be understood that the NNU121, including the embodiment described with respect to FIG. 49, may beemployed to efficiently perform computations associated with other LSTMcells than that described in FIG. 46.

Preferably, the NNU 121 may be employed to perform computations for arecurrent neural network that includes a layer of LSTM cells connectedto other layers. For example, in the LSTM tutorial, the network includesa mean pooling layer that receives the outputs (H) of the LSTM cells ofthe LSTM layer and a logistic regression layer that receives the outputof the mean pooling layer.

Referring now to FIG. 46, a block diagram illustrating an embodiment ofan LSTM cell 4600 is shown.

The LSTM cell 4600 includes a memory cell input (X), a memory celloutput (H), an input gate (I), an output gate (O), a forget gate (F), acell state (C) and a candidate cell state (C′), as shown. The input gate(I) gates the memory cell input (X) to the cell state (C) and the outputgate (O) gates the cell state (C) to the memory cell output (H). Thecell state (C) is fed back as the candidate cell state (C′) of a timestep. The forget gate (F) gates the candidate cell state (C′) which isfed back and become the cell state (C) for the next time step.

In the embodiment of FIG. 46, the following equations are used tocompute the various values specified above:

I=SIGMOID (Wi* +Ui*H+Bi)   (1)

F=SIGMOID (Wf*X+Uf*H+Bf)   (2)

C′=TANH (Wc*X+Uc*H+Bc)   (3)

C=I*C′+F*C   (4)

O=SIGMOID (Wo*X+Uo*H+Bo)   (5)

H=O*TANH (C)   (6)

Wi and Ui are weight values associated with the input gate (I) and Bi isa bias value associated with the input gate (I). Wf and Uf are weightvalues associated with the forget gate (F) and Bf is a bias valueassociated with the forget gate (F). Wo and Uo are weight valuesassociated with the output gate (O) and Bo is a bias value associatedwith the output gate (O). As shown, equations (1), (2) and (5) computethe input gate (I), forget gate (F), and output gate (O), respectively.Equation (3) computes the candidate cell state (C′), and equation (4)computes the candidate cell state (C′) using the current cell state (C)as input, i.e., using the cell state (C) of the current time step.Equation (6) computes the cell output (H). Other embodiments of an LSTMcell that employ different computations for the input gate, forget gate,output gate, candidate cell state, cell state and cell output arecontemplated.

For purposes of the present disclosure, an LSTM cell comprises a memorycell input, a memory cell output, a cell state, a candidate cell state,an input gate, an output gate and a forget gate. For each time step, theinput gate, output gate, forget gate and candidate cell state arefunctions of the current time step memory cell input and the previoustime step memory cell output and associated weights. The cell state ofthe time step is a function of the previous time step cell state, thecandidate cell state, the input gate and the forget gate. In this sense,the cell state is fed back and used in the computation of the next timestep cell state. The memory cell output of the time step is a functionof the cell state computed for the time step and the output gate. AnLSTM network is a neural network that includes a layer of LSTM cells.

Referring now to FIG. 47, a block diagram illustrating an example of thelayout of data within the data RAM 122 and weight RAM 124 of the NNU 121as it performs calculations associated with a layer of 128 LSTM cells4600 of FIG. 46 is shown. In the example of FIG. 47, the NNU 121 isconfigured as 512 NPUs 126, or neurons, e.g., in a wide configuration,however the values generated by only 128 NPUs 126 (e.g., NPUs 0 through127) are used since in the example there are only 128 LSTM cells 4600 inthe LSTM layer.

As shown, the weight RAM 124 holds weight, bias and intermediate valuesfor corresponding NPUs 0 through 127 of the NNU 121. Columns 0 through127 of the weight RAM 124 hold weight, bias and intermediate values forcorresponding NPUs 0 through 127 of the NNU 121. Rows 0 through 14 eachhold 128 of the following respective values of equations (1) through (6)above for provision to NPUs 0 through 127: Wi, Ui, Bi, Wf, Uf, Bf, Wc,Uc, Bc, C′, TANH(C), C, Wo, Uo, Bo. Preferably, the weight and biasvalues—Wi, Ui, Bi, Wf, Uf, Bf, Wc, Uc, Bc, Wo, Uo, Bo (in rows 0 through8 and 12 through 14)—are written/populated in the weight RAM 124 by thearchitectural program running on the processor 100 via MTNN instructions1400 and are read/used by the non-architectural program running on theNNU 121, such as the non-architectural program of FIG. 48. Preferably,the intermediate values—C′, TANH(C), C (in rows 9 through 11)—arewritten/populated in the weight RAM 124 and are also read/used by thenon-architectural program running on the NNU 121, as described in moredetail below.

As shown, the data RAM 122 holds input (X), output (H), input gate (I),forget gate (F) and output gate (O) values for a sequence of time steps.More specifically, a quintuplet of five rows holds the X, H, I, F and 0values for a given time step. In an embodiment in which the data RAM 122has 64 rows, the data RAM 122 can hold the cell values for 12 differenttime steps, as shown. In the example of FIG. 47, rows 0 through 4 holdthe cell values for time step 0, rows 5 through 9 hold the cell valuesfor time step 1, and so forth to rows 55 through 59 hold the cell valuesfor time step 11. The first row of a quintuplet holds the X values ofthe time step. The second row of a quintuplet holds the H values of thetime step. The third row of a quintuplet holds the I values of the timestep. The fourth row of a quintuplet holds the F values of the timestep. The fifth row of a quintuplet holds the 0 values of the time step.As shown, each column in the data RAM 122 holds the values for itscorresponding neurons, or NPUs 126. That is, column 0 holds the valuesassociated with LSTM cell 0, whose computations are performed by NPU 0;column 1 holds the values associated with LSTM cell 1, whosecomputations are performed by NPU 1; and so forth to column 127 holdsthe values associated with LSTM cell 127, whose computations areperformed by NPU 127, as described in more detail below with respect toFIG. 48.

Preferably, the X values (in rows 0, 5, 9 and so forth to 55) arewritten/populated in the data RAM 122 by the architectural programrunning on the processor 100 via MTNN instructions 1400 and areread/used by the non-architectural program running on the NNU 121, suchas the non-architectural program of FIG. 48. Preferably, the I, F and 0values (in rows 2/3/4, 7/8/9, 12/13/14 and so forth to 57/58/59) arewritten/populated in the data RAM 122 and are also read/used by thenon-architectural program running on the NNU 121, as described in moredetail below. Preferably, the H values (in rows 1, 6, 10 and so forth to56) are written/populated in the data RAM 122 and are also read/used bythe non-architectural program running on the NNU 121, and are read bythe architectural program running on the processor 100 via MFNNinstructions 1500.

The example of FIG. 47 assumes the architectural program: (1) populatesthe data RAM 122 with the input X values for 12 different time steps(rows 0, 5, and so forth to 55); (2) starts the non-architecturalprogram of FIG. 48; (3) detects the non-architectural program hascompleted; (4) reads out of the data RAM 122 the output H values (rows1, 6, and so forth to 59); and (5) repeats steps (1) through (4) as manytimes as needed to complete a task, e.g., computations used to performthe recognition of a statement made by a user of a mobile phone.

In an alternative approach, the architectural program: (1) populates thedata RAM 122 with the input X values for a single time step (e.g., row0); (2) starts the non-architectural program (a modified version of FIG.48 that does not require the loop and accesses a single quintuplet ofdata RAM 122 rows); (3) detects the non-architectural program hascompleted; (4) reads out of the data RAM 122 the output H values (e.g.,row 1); and (5) repeats steps (1) through (4) as many times as needed tocomplete a task. Either of the two approaches may be preferabledepending upon the manner in which the input X values to the LSTM layerare sampled. For example, if the task tolerates sampling the input formultiple time steps (e.g., on the order of 12) and performing thecomputations, then the first approach may be preferable since it islikely more computational resource efficient and/or higher performance,whereas, if the task cannot only tolerate sampling at a single timestep, the second approach may be required.

A third embodiment is contemplated that is similar to the secondapproach but in which, rather than using a single quintuplet of data RAM122 rows, the non-architectural program uses multiple quintuplet ofrows, i.e., a different quintuplet for each time step, similar to thefirst approach. In the third embodiment, preferably the architecturalprogram includes a step prior to step (2) in which it updates thenon-architectural program before starting it, e.g., by updating the dataRAM 122 row in the instruction at address 0 to point to the nextquintuplet.

Referring now to FIG. 48, a table illustrating a program for storage inthe program memory 129 of and execution by the NNU 121 to accomplishcomputations associated with an LSTM cell layer and using data andweights according to the arrangement of FIG. 47 is shown. The exampleprogram of FIG. 48 includes 24 non-architectural instructions ataddresses 0 through 23. The instruction at address 0 (INITIALIZE NPU,CLR ACC, LOOPCNT=12, DR IN ROW=−1, DR OUT ROW=2) clears the accumulator202 and initializes the loop counter 3804 to a value of 12 to cause theloop body (the instructions of addresses 1 through 22) to be performed12 times. The initialize instruction also initializes the data RAM 122row to be read (e.g., register 2608 of FIGS. 26/39) to a value of −1,which will be incremented to zero by the first execution instance of theinstruction at address 1. The initialize instruction also initializesthe data RAM 122 row to be written (e.g., register 2606 of FIGS. 26/39)to row 2. Preferably, the initialize instruction also puts the NNU 121in a wide configuration such that the NNU 121 is configured as 512 NPUs126. As may be observed from the description below, 128 of the 512 NPUs126 correspond to and operate as 128 LSTM cells 4600 during theexecution of the instructions of addresses 0 through 23.

During the first execution instance of the instructions at addresses 1through 4, each of the 128 NPUs 126 (i.e., NPUs 126 0 through 127)computes the input gate (I) value for its corresponding LSTM cell 4600for the first time step (time step 0) and writes the I value to thecorresponding word of row 2 of the data RAM 122; during the secondexecution instance of the instructions at addresses 1 through 4, each ofthe 128 NPUs 126 computes the I value for its corresponding LSTM cell4600 for the second time step (time step 1) and writes the I value tothe corresponding word of row 7 of the data RAM 122; and so forth untilduring the twelfth execution instance of the instructions at addresses 1through 4, each of the 128 NPUs 126 computes the I value for itscorresponding LSTM cell 4600 for the twelfth time step (time step 11)and writes the I value to the corresponding word of row 57 of the dataRAM 122, as shown in FIG. 47.

More specifically, the multiply-accumulate instruction at address 1reads the next row after the current data RAM 122 row (row 0 duringfirst execution instance, row 5 during second execution instance, and soforth to row 55 of the twelfth execution instance) that contains thecell input (X) values associated with the current time step and readsrow 0 of the weight RAM 124 that contains the Wi values and multipliesthem to generate a first product accumulated into the accumulator 202,which was just cleared by either the initialize instruction at address 0or the instruction at address 22. Next, the multiply-accumulateinstruction at address 2 reads the next data RAM 122 row (row 1 duringfirst execution instance, row 6 during second execution instance, and soforth to row 56 of the twelfth execution instance) that contains thecell output (H) values associated with the current time step and readsrow 1 of the weight RAM 124 that contains the Ui values and multipliesthem to generate a second product added to the accumulator 202. The Hvalues associated with the current time step, which are read from thedata RAM 122 by the instruction at address 2 (and the instructions ataddresses 6, 10 and 18), are generated during the previous time step andwritten to the data RAM 122 by the output instruction at address 22;however, in the case of the first execution instance of the instructionat address 2, the H values in row 1 of the data RAM 122 are written withan initial value. Preferably the architectural program (e.g., using aMTNN instruction 1400) writes the initial H values to row 1 of the dataRAM 122 prior to starting the non-architectural program of FIG. 48;however, other embodiments are contemplated in which thenon-architectural program includes initial instructions that write theinitial H values to row 1 of the data RAM 122. In one embodiment, theinitial H values are zero. Next, the add weight word to accumulatorinstruction at address 3 (ADD_W_ACC WR ROW 2) reads row 2 of the weightRAM 124 that contains the Bi values and adds them to the accumulator202. Finally, the output instruction at address 4 (OUTPUT SIGMOID, DROUT ROW+0, CLR ACC) performs a sigmoid activation function on theaccumulator 202 values and writes the results to the current data RAM122 output row (row 2 for the first execution instance, row 7 for thesecond execution instance, and so forth to row 57 for the twelfthexecution instance) and clears the accumulator 202.

During the first execution instance of the instructions at addresses 5through 8, each of the 128 NPUs 126 computes the forget gate (F) valuefor its corresponding LSTM cell 4600 for the first time step (time step0) and writes the F value to the corresponding word of row 3 of the dataRAM 122; during the second execution instance of the instructions ataddresses 5 through 8, each of the 128 NPUs 126 computes the F value forits corresponding LSTM cell 4600 for the second time step (time step 1)and writes the F value to the corresponding word of row 8 of the dataRAM 122; and so forth until during the twelfth execution instance of theinstructions at addresses 5 through 8, each of the 128 NPUs 126 computesthe F value for its corresponding LSTM cell 4600 for the twelfth timestep (time step 11) and writes the F value to the corresponding word ofrow 58 of the data RAM 122, as shown in FIG. 47. The instructions ataddresses 5 through 8 compute the F value in a manner similar to theinstructions at addresses 1 through 4 as described above, however theinstructions at addresses 5 through 7 read the Wf, Uf and Bf values fromrows 3, 4 and 5, respectively, of the weight RAM 124 to perform themultiply and/or add operations.

During the twelve execution instances of the instructions at addresses 9through 12, each of the 128 NPUs 126 computes the candidate cell state(C′) value for its corresponding LSTM cell 4600 for a corresponding timestep and writes the C′ value to the corresponding word of row 9 of theweight RAM 124. The instructions at addresses 9 through 12 compute theC′ value in a manner similar to the instructions at addresses 1 through4 as described above, however the instructions at addresses 9 through 11read the Wc, Uc and Bc values from rows 6, 7 and 8, respectively, of theweight RAM 124 to perform the multiply and/or add operations.Additionally, the output instruction at address 12 performs a tanhactivation function rather than a sigmoid activation function (as theoutput instruction at address 4 does).

More specifically, the multiply-accumulate instruction at address 9reads the current data RAM 122 row (row 0 during first executioninstance, row 5 during second execution instance, and so forth to row 55of the twelfth execution instance) that contains the cell input (X)values associated with the current time step and reads row 6 of theweight RAM 124 that contains the We values and multiplies them togenerate a first product accumulated into the accumulator 202, which wasjust cleared by the instruction at address 8. Next, themultiply-accumulate instruction at address 10 reads the next data RAM122 row (row 1 during first execution instance, row 6 during secondexecution instance, and so forth to row 56 of the twelfth executioninstance) that contains the cell output (H) values associated with thecurrent time step and reads row 7 of the weight RAM 124 that containsthe Uc values and multiplies them to generate a second product added tothe accumulator 202. Next, the add weight word to accumulatorinstruction at address 11 reads row 8 of the weight RAM 124 thatcontains the Bc values and adds them to the accumulator 202. Finally,the output instruction at address 12 (OUTPUT TANH, WR OUT ROW 9, CLRACC) performs a tanh activation function on the accumulator 202 valuesand writes the results to row 9 of the weight RAM 124 and clears theaccumulator 202.

During the twelve execution instances of the instructions at addresses13 through 16, each of the 128 NPUs 126 computes the new cell state (C)value for its corresponding LSTM cell 4600 for a corresponding time stepand writes the new C value to the corresponding word of row 11 of theweight RAM 124 and computes tanh(C) and writes it to the correspondingword of row 10 of the weight RAM 124. More specifically, themultiply-accumulate instruction at address 13 reads the next row afterthe current data RAM 122 row (row 2 during the first execution instance,row 7 during the second execution instance, and so forth to row 57 ofthe twelfth execution instance) that contains the input gate (I) valuesassociated with the current time step and reads row 9 of the weight RAM124 that contains the candidate cell state (C′) values (just written bythe instruction at address 12) and multiplies them to generate a firstproduct accumulated into the accumulator 202, which was just cleared bythe instruction at address 12. Next, the multiply-accumulate instructionat address 14 reads the next data RAM 122 row (row 3 during firstexecution instance, row 8 during second execution instance, and so forthto row 58 of the twelfth execution instance) that contains the forgetgate (F) values associated with the current time step and reads row 11of the weight RAM 124 that contains the current cell state (C) valuescomputed during the previous time step (written by the most recentexecution instance of the instruction at address 15) and multiplies themto generate a second product added to the accumulator 202. Next, theoutput instruction at address 15 (OUTPUT PASSTHRU, WR OUT ROW 11) passesthrough the accumulator 202 values and writes them to row 11 of theweight RAM 124. It should be understood that the C value read from row11 of the data RAM 122 by the instruction at address 14 is the C valuegenerated and written by the most recent execution instance of theinstructions at addresses 13 through 15. The output instruction ataddress 15 does not clear the accumulator 202 so that their values canbe used by the instruction at address 16. Finally, the outputinstruction at address 16 (OUTPUT TANH, WR OUT ROW 10, CLR ACC) performsa tanh activation function on the accumulator 202 values and writes theresults to row 10 of the weight RAM 124 for use by the instruction ataddress 21 that computes the cell output (H) values. The instruction ataddress 16 clears the accumulator 202.

During the first execution instance of the instructions at addresses 17through 20, each of the 128 NPUs 126 computes the output gate (O) valuefor its corresponding LSTM cell 4600 for the first time step (time step0) and writes the 0 value to the corresponding word of row 4 of the dataRAM 122; during the second execution instance of the instructions ataddresses 17 through 20, each of the 128 NPUs 126 computes the 0 valuefor its corresponding LSTM cell 4600 for the second time step (time step1) and writes the O value to the corresponding word of row 9 of the dataRAM 122; and so forth until during the twelfth execution instance of theinstructions at addresses 17 through 20, each of the 128 NPUs 126computes the O value for its corresponding LSTM cell 4600 for thetwelfth time step (time step 11) and writes the O value to thecorresponding word of row 58 of the data RAM 122, as shown in FIG. 47.The instructions at addresses 17 through 20 compute the O value in amanner similar to the instructions at addresses 1 through 4 as describedabove, however the instructions at addresses 17 through 19 read the Wo,Uo and Bo values from rows 12, 13 and 14, respectively, of the weightRAM 124 to perform the multiply and/or add operations.

During the first execution instance of the instructions at addresses 21through 22, each of the 128 NPUs 126 computes the cell output (H) valuefor its corresponding LSTM cell 4600 for the first time step (time step0) and writes the H value to the corresponding word of row 6 of the dataRAM 122; during the second execution instance of the instructions ataddresses 21 through 22, each of the 128 NPUs 126 computes the H valuefor its corresponding LSTM cell 4600 for the second time step (time step1) and writes the H value to the corresponding word of row 11 of thedata RAM 122; and so forth until during the twelfth execution instanceof the instructions at addresses 21 through 22, each of the 128 NPUs 126computes the H value for its corresponding LSTM cell 4600 for thetwelfth time step (time step 11) and writes the H value to thecorresponding word of row 60 of the data RAM 122, as shown in FIG. 47.

More specifically, the multiply-accumulate instruction at address 21reads the third next row after the current data RAM 122 row (row 4during first execution instance, row 9 during second execution instance,and so forth to row 59 during the twelfth execution instance) thatcontains the output gate (O) values associated with the current timestep and reads row 10 of the weight RAM 124 that contains the tanh(C)values (written by the instruction at address 16) and multiplies them togenerate a product accumulated into the accumulator 202, which was justcleared by the instruction at address 20. Then, the output instructionat address 22 passes through the accumulator 202 values and writes themto the second next output row 11 of the data RAM 122 (row 6 during thefirst execution instance, row 11 during the first execution instance,and so forth to row 61 during the twelfth execution instance) and clearsthe accumulator 202. It should be understood that the H value written toa row of the data RAM 122 by the instruction at address 22 (row 6 duringthe first execution instance, row 11 during the second executioninstance, and so forth to row 61 of the twelfth execution instance) isthe H value consumed/read by the following execution instance of theinstructions at addresses 2, 6, 10 and 18. However, the H value writtento row 61 of the twelfth execution instance is not consumed/read by anexecution instance of the instructions at addresses 2, 6, 10 and 18;rather, preferably it is consumed/read by the architectural program.

The instruction at address 23 (LOOP 1) decrements the loop counter 3804and loops back to the instruction at address 1 if the new the loopcounter 3804 value is greater than zero.

Referring now to FIG. 49, a block diagram illustrating an NNU 121embodiment with output buffer masking and feedback capability within NPUgroups is shown. FIG. 49 illustrates a single NPU group 4901 of fourNPUs 126. Although FIG. 49 illustrates a single NPU group 4901, itshould be understood that each of the NPUs 126 of the NNU 121 isincluded in a NPU group 4901 such that there are N/J NPU groups 4901,where N is the number of NPUs 126 (e.g., 512 in a wide configuration or1024 in a narrow configuration) and J is the number of NPUs 126 in agroup 4901 (e.g., four in the embodiment of FIG. 49). FIG. 49 refers tothe four NPUs 126 of the NPU group 4901 as NPU 0, NPU 1, NPU 2 and NPU3.

Each NPU 126 in the embodiment of FIG. 49 is similar to the NPU 126described with respect to FIG. 7 above and like-numbered elements aresimilar. However, the mux-reg 208 is modified to include four additionalinputs 4905, the mux-reg 705 is modified to include four additionalinputs 4907, the selection input 213 is modified to select from amongthe original inputs 211 and 207 as well as the additional inputs 4905for provision on output 209, and the selection input 713 is modified toselect from among the original inputs 711 and 206 as well as theadditional inputs 4907 for provision on output 203.

A portion of the row buffer 1104 of FIG. 11, referred to as outputbuffer 1104 in FIG. 49, is shown. More specifically, words 0, 1, 2, and3 of the output buffer 1104 are shown, which receive the respectiveoutputs of the four AFUs 212 associated with NPUs 0, 1, 2, and 3. Theportion of the output buffer 1104 comprising N words corresponding to anNPU group 4901 is referred to as an output buffer word group. In theembodiment of FIG. 49, N is four. The four words of the output buffer1104 are fed back and received as the four additional inputs 4905 to themux-reg 208 and as the four additional inputs 4907 to the mux-reg 705.The feeding back of output buffer word groups to their respective NPUgroups 4901 provides the ability for an arithmetic instruction of anon-architectural program to select for its inputs one or two of thewords of the output buffer 1104 associated with the NPU group 4901(i.e., of the output buffer word group), examples of which are describedbelow with respect to the non-architectural program of FIG. 51, e.g., ataddresses 4, 8, 11, 12 and 15. That is, the word of the output buffer1104 specified in the non-architectural instruction determines the valuegenerated on the selection inputs 213/713. This capability effectivelyenables the output buffer 1104 to serve as a scratch pad memory ofsorts, which may enable a non-architectural program to reduce the numberof writes to the data RAM 122 and/or weight RAM 124 and subsequent readstherefrom, e.g., of intermediately generated and used values.Preferably, the output buffer 1104, or row buffer 1104, comprises aone-dimensional array of registers that may be configured to storeeither 1024 narrow words or 512 wide words. Preferably, the outputbuffer 1104 may be read in a single clock cycle and written in a singleclock cycle. Unlike the data RAM 122 and weight RAM 124, which areaccessible by both the architectural program and the non-architecturalprogram, the output buffer 1104 is not accessible by the architecturalprogram, but is instead only accessible by the non-architecturalprogram.

The output buffer 1104 is modified to receive a mask input 4903.Preferably, the mask input 4903 includes four bits corresponding to thefour words of the output buffer 1104 associated with the four NPUs 126of the NPU group 4901. Preferably, if the mask input 4903 bitcorresponding to a word of the output buffer 1104 is true, the word ofthe output buffer 1104 retains its current value; otherwise, the word ofthe output buffer 1104 is updated with the AFU 212 output. That is, ifthe mask input 4903 bit corresponding to a word of the output buffer1104 is false, the AFU 212 output is written to the word of the outputbuffer 1104. This provides the ability for an output instruction of anon-architectural program to selectively write the AFU 212 output tosome words of the output buffer 1104 and to retain the current values ofother words of the output buffer 1104, examples of which are describedbelow with respect to the instructions of the non-architectural programof FIG. 51, e.g., at addresses 6, 10, 13 and 14. That is, the words ofthe output buffer 1104 specified in the non-architectural instructiondetermine the value generated on the mask input 4903.

For simplicity, FIG. 49 does not show the inputs 1811 (of FIGS. 18, 19and 23, for example) to the mux-regs 208/705. However, embodiments arecontemplated that support both dynamically configurable NPUs 126 andfeedback/masking of the output buffer 1104. Preferably, in suchembodiments the output buffer word groups are correspondinglydynamically configurable.

It should be understood that although an embodiment is described inwhich the number of NPUs 126 in a NPU group 4901 is four, otherembodiments are contemplated in which the number is greater or smaller.Furthermore, in an embodiment that includes shared AFUs 1112, such asshown in FIG. 52, there may be a synergistic relationship between thenumber of NPUs 126 in a NPU group 4901 and the number of NPUs 126 in anAFU 212 group. The output buffer 1104 masking and feedback capabilitywithin NPU groups is particularly beneficial for efficiently performingcomputations associated with LSTM cells 4600, as described in moredetail with respect to FIGS. 50 and 51.

Referring now to FIG. 50, a block diagram illustrating an example of thelayout of data within the data RAM 122, weight RAM 124 and output buffer1104 of the NNU 121 of FIG. 49 as it performs calculations associatedwith a layer of 128 LSTM cells 4600 of FIG. 46 is shown. In the exampleof FIG. 50, the NNU 121 is configured as 512 NPUs 126, or neurons, e.g.,in a wide configuration. Like the example of FIGS. 47 and 48, in theexample of FIGS. 50 and 51 there are only 128 LSTM cells 4600 in theLSTM layer. However, in the example of FIG. 50, the values generated byall 512 NPUs 126 (e.g., NPUs 0 through 127) are used. Advantageously,each NPU group 4901 operates collectively as an LSTM cell 4600 whenexecuting the non-architectural program of FIG. 51.

As shown, the data RAM 122 holds cell input (X) and output (H) valuesfor a sequence of time steps. More specifically, a pair of two rowsholds the X and H values for a given time step. In an embodiment inwhich the data RAM 122 has 64 rows, the data RAM 122 can hold the cellvalues for 31 different time steps, as shown. In the example of FIG. 50,rows 2 and 3 hold the values for time step 0, rows 4 and 5 hold the cellvalues for time step 1, and so forth to rows 62 and 63 hold the cellvalues for time step 30. The first row of a pair holds the X values ofthe time step and the second row of a pair holds the H values of thetime step. As shown, each group of four columns corresponding to a NPUgroup 4901 in the data RAM 122 holds the values for its correspondingLSTM cell 4600. That is, columns 0-3 hold the values associated withLSTM cell 0, whose computations are performed by NPUs 0-3, i.e., NPUgroup 0; columns 4-7 hold the values associated with LSTM cell 1, whosecomputations are performed by NPUs 4-7, i.e., NPU group 1; and so forthto columns 508-511 hold the values associated with LSTM cell 127, whosecomputations are performed by NPUs 508-511, i.e., NPU group 127, asdescribed in more detail below with respect to FIG. 51. As shown, row 1is unused, and row 0 holds initial cell output (H) values, preferablypopulated by the architectural program with zero values, althoughembodiments are contemplated in which initial instructions of thenon-architectural populate the initial cell output (H) values of row 0.

Preferably, the X values (in rows 2, 4, 6 and so forth to 62) arewritten/populated in the data RAM 122 by the architectural programrunning on the processor 100 via MTNN instructions 1400 and areread/used by the non-architectural program running on the NNU 121, suchas the non-architectural program of FIG. 50. Preferably, the H values(in rows 3, 5, 7 and so forth to 63) are written/populated in the dataRAM 122 and are also read/used by the non-architectural program runningon the NNU 121, as described in more detail below. Preferably, the Hvalues are also read by the architectural program running on theprocessor 100 via MFNN instructions 1500. It is noted that thenon-architectural program of FIG. 51 assumes that within each group offour columns corresponding to a NPU group 4901 (e.g., columns 0-3, 4-7,5-8 and so forth to 508-511) the four X values in a given row arepopulated (e.g., by the architectural program) with the same value.Similarly, the non-architectural program of FIG. 51 computes and writeswithin each group of four columns corresponding to a NPU group 4901 in agiven row the same value for the four H values.

As shown, the weight RAM 124 holds weight, bias and cell state (C)values for the NPUs of the NNU 121. Within each group of four columnscorresponding to a NPU group 4901 (e.g., columns 0-3, 4-7, 5-8 and soforth to 508-511): (1) the column whose index mod 4 equals 3, holds theWc, Uc, Bc, and C values in rows 0, 1, 2, and 6, respectively; (2) thecolumn whose index mod 4 equals 2, holds the Wo, Uo, and Bo values inrows 3, 4, and 5, respectively; (3) the column whose index mod 4 equals1, holds the Wf, Uf, and Bf values in rows 3, 4, and 5, respectively;and (4) the column whose index mod 4 equals 0, holds the Wi, Ui, and Bivalues in rows 3, 4, and 5, respectively. Preferably, the weight andbias values—Wi, Ui, Bi, Wf, Uf, Bf, Wc, Uc, Bc, Wo, Uo, Bo (in rows 0through 5)—are written/populated in the weight RAM 124 by thearchitectural program running on the processor 100 via MTNN instructions1400 and are read/used by the non-architectural program running on theNNU 121, such as the non-architectural program of FIG. 51. Preferably,the intermediate C values are written/populated in the weight RAM 124and are read/used by the non-architectural program running on the NNU121, as described in more detail below.

The example of FIG. 50 assumes the architectural program: (1) populatesthe data RAM 122 with the input X values for 31 different time steps(rows 2, 4, and so forth to 62); (2) starts the non-architecturalprogram of FIG. 51; (3) detects the non-architectural program hascompleted; (4) reads out of the data RAM 122 the output H values (rows3, 5, and so forth to 63); and (5) repeats steps (1) through (4) as manytimes as needed to complete a task, e.g., computations used to performthe recognition of a statement made by a user of a mobile phone.

In an alternative approach, the architectural program: (1) populates thedata RAM 122 with the input X values for a single time step (e.g., row2); (2) starts the non-architectural program (a modified version of FIG.51 that does not require the loop and accesses a single pair of data RAM122 rows); (3) detects the non-architectural program has completed; (4)reads out of the data RAM 122 the output H values (e.g., row 3); and (5)repeats steps (1) through (4) as many times as needed to complete atask. Either of the two approaches may be preferable depending upon themanner in which the input X values to the LSTM layer are sampled. Forexample, if the task tolerates sampling the input for multiple timesteps (e.g., on the order of 31) and performing the computations, thenthe first approach may be preferable since it is likely morecomputational resource efficient and/or higher performance, whereas, ifthe task cannot only tolerate sampling at a single time step, the secondapproach may be required.

A third embodiment is contemplated that is similar to the secondapproach but in which, rather than using a single pair of data RAM 122rows, the non-architectural program uses multiple pair of rows, i.e., adifferent pair for each time step, similar to the first approach. In thethird embodiment, preferably the architectural program includes a stepprior to step (2) in which it updates the non-architectural programbefore starting it, e.g., by updating the data RAM 122 row in theinstruction at address 1 to point to the next pair.

As shown, the output buffer 1104 holds intermediate values of the celloutput (H), candidate cell state (C′), input gate (I), forget gate (F),output gate (O), cell state (C), and tanh(C) after the execution of aninstruction at different addresses of the non-architectural program ofFIG. 51 for corresponding NPUs 0 through 511 of the NNU 121, as shown.Within each output buffer word group (e.g., group of four words of theoutput buffer 1104 corresponding to a NPU group 4901, e.g., words 0-3,4-7, 5-8 and so forth to 508-511), the word whose index mod 4 equals 3is referred to as OUTBUF[3], the word whose index mod 4 equals 2 isreferred to as OUTBUF[2], the word whose index mod 4 equals 1 isreferred to as OUTBUF[1], and the word whose index mod 4 equals 0 isreferred to as OUTBUF[0].

As shown, after execution of the instruction at address 2 of thenon-architectural program of FIG. 51, for each NPU group 4901, all fourwords of the output buffer 1104 are written with the initial cell output(H) values for the corresponding LSTM cell 4600. After execution of theinstruction at address 6, for each NPU group 4901, OUTBUF[3] is writtenwith the candidate cell state (C′) value for the corresponding LSTM cell4600 and the other three words of the output buffer 1104 retain theirprevious values. After execution of the instruction at address 10, foreach NPU group 4901, OUTBUF[0] is written with the input gate (I) value,OUTBUF[1] is written with the forget gate (F) value, OUTBUF[2] iswritten with the output gate (O) value, for the corresponding LSTM cell4600, and OUTBUF[3] retains its previous value. After execution of theinstruction at address 13, for each NPU group 4901, OUTBUF[3] is writtenwith the new cell state (C) value (as the output buffer 1104, includingthe C value in slot 3, is written to row 6 of the weight RAM 124, asdescribed in more detail below with respect to FIG. 51) for thecorresponding LSTM cell 4600 and the other three words of the outputbuffer 1104 retain their previous values. After execution of theinstruction at address 14, for each NPU group 4901, OUTBUF[3] is writtenwith the tanh(C) value for the corresponding LSTM cell 4600 and theother three words of the output buffer 1104 retain their previousvalues. After execution of the instruction at address 16, for each NPUgroup 4901, all four words of the output buffer 1104 are written withthe new cell output (H) values for the corresponding LSTM cell 4600. Thepattern repeats from address 6 through address 16 (i.e., excluding theexecution at address 2, since it is outside the program loop) thirtymore times as the program loops at address 17 back to address 3.

Referring now to FIG. 51, a table illustrating a program for storage inthe program memory 129 of and execution by the NNU 121 of FIG. 49 toaccomplish computations associated with an LSTM cell layer and usingdata and weights according to the arrangement of FIG. 50 is shown. Theexample program of FIG. 51 includes 18 non-architectural instructions ataddresses 0 through 17. The instruction at address 0 is an initializeinstruction that clears the accumulator 202 and initializes the loopcounter 3804 to a value of 31 to cause the loop body (the instructionsof addresses 1 through 17) to be performed 31 times. The initializeinstruction also initializes the data RAM 122 row to be written (e.g.,register 2606 of FIGS. 26/39) to a value of 1, which will be incrementedto 3 by the first execution instance of the instruction at address 16.Preferably, the initialize instruction also puts the NNU 121 in a wideconfiguration such that the NNU 121 is configured as 512 NPUs 126. Asmay be observed from the description below, each of the 128 NPU groups4901 of the 512 NPUs 126 correspond to and operate as one of the 128LSTM cells 4600 during the execution of the instructions of addresses 0through 17.

The instructions at addresses 1 and 2 are outside the loop body andexecute only once. They generate and write the initial cell output (H)value (e.g., zero value) to all words of the output buffer 1104. Theinstruction at address 1 reads the initial H values from row 0 of thedata RAM 122 and puts them into the accumulator 202, which was clearedby the instruction at address 0. The instruction at address 2 (OUTPUTPASSTHRU, NOP, CLR ACC) passes through the accumulator 202 value to theoutput buffer 1104, as shown in FIG. 50. The designation of the “NOP” inthe output instruction at address 2 (and other output instructions ofFIG. 51) indicates that the value being output is written only to theoutput buffer 1104 but not written to memory, i.e., neither to the dataRAM 122 nor to the weight RAM 124. The instruction at address 2 alsoclears the accumulator 202.

The instructions at addresses 3 through 17 are inside the loop body andexecute the loop count number of times (e.g., 31).

Each execution instance of the instructions at addresses 3 through 6computes and writes the tanh(C′) value for the current time step toOUTBUF[3], which will be used by the instruction at address 11. Morespecifically, the multiply-accumulate instruction at address 3 reads thecell input (X) value associated with the time step from the current dataRAM 122 read row (e.g., 2, 4, 6 and so forth to 62) and reads the Wevalues from row 0 of the weight RAM 124 and multiplies them to generatea product added to the accumulator 202, which was cleared by theinstruction at address 2.

The multiply-accumulate instruction at address 4 (MULT-ACCUM OUTBUF[0],WR ROW 1) reads (i.e., all 4 NPUs 126 of the NPU group 4901) the H valuefrom OUTBUF[0] and reads the Uc values from row 1 of the weight RAM 124and multiplies them to generate a second product added to theaccumulator 202.

The add weight word to accumulator instruction at address 5 (ADD_W_ACCWR ROW 2) reads the Bc values from row 2 of the weight RAM 124 and addsthem to the accumulator 202.

The output instruction at address 6 (OUTPUT TANH, NOP, MASK[0:2], CLRACC) performs a tanh activation function on the accumulator 202 valueand the result is written only to OUTBUF[3] (i.e., only the NPU 126 ofthe NPU group 4901 whose index mod 4 equals 3 writes its result), andthe accumulator 202 is cleared. That is, the output instruction ataddress 6 masks OUTBUF[0], OUTBUF[1] and OUTBUF[2] (as indicated by theMASK[0:2] nomenclature) to cause them to retain their current values, asshown in FIG. 50. Additionally, the output instruction at address 6 doesnot write to memory (as indicated by the NOP nomenclature).

Each execution instance of the instructions at addresses 7 through 10computes and writes the input gate (I), forget gate (F), and output gate(O) values for the current time step to OUTBUF[0], OUTBUF[1], OUTBUF[2],respectively, which will be used by the instructions at addresses 11,12, and 15, respectively. More specifically, the multiply-accumulateinstruction at address 7 reads the cell input (X) value associated withthe time step from the current data RAM 122 read row (e.g., 2, 4, 6 andso forth to 62) and reads the Wi, Wf, and Wo values from row 3 of theweight RAM 124 and multiplies them to generate a product added to theaccumulator 202, which was cleared by the instruction at address 6. Morespecifically, within an NPU group 4901, the NPU 126 whose index mod 4equals 0 computes the product of X and Wi, the NPU 126 whose index mod 4equals 1 computes the product of X and Wf, and the NPU 126 whose indexmod 4 equals 2 computes the product of X and Wo.

The multiply-accumulate instruction at address 8 reads (i.e., all 4 NPUs126 of the NPU group 4901) the H value from OUTBUF[0] and reads the Ui,Uf, and Uo values from row 4 of the weight RAM 124 and multiplies themto generate a second product added to the accumulator 202. Morespecifically, within an NPU group 4901, the NPU 126 whose index mod 4equals 0 computes the product of H and Ui, the NPU 126 whose index mod 4equals 1 computes the product of H and Uf, and the NPU 126 whose indexmod 4 equals 2 computes the product of H and Uo.

The add weight word to accumulator instruction at address 9 reads theBi, Bf, and Bo values from row 5 of the weight RAM 124 and adds them tothe accumulator 202. More specifically, within an NPU group 4901, theNPU 126 whose index mod 4 equals 0 adds the Bi value, the NPU 126 whoseindex mod 4 equals 1 adds the Bf value, and the NPU 126 whose index mod4 equals 2 adds the Bo value.

The output instruction at address 10 (OUTPUT SIGMOID, NOP, MASK[3], CLRACC) performs a sigmoid activation function on the accumulator 202 valueand writes the computed I, F and O values to OUTBUF[0], OUTBUF[1], andOUTBUF[2], respectively, and clears the accumulator 202, without writingto memory. That is, the output instruction at address 10 masks OUTBUF[3](as indicated by the MASK[3] nomenclature) to cause it to retain itscurrent value (which is C′) , as shown in FIG. 50.

Each execution instance of the instructions at addresses 11 through 13computes and writes the new cell state (C) values generated by thecurrent time step to row 6 of the weight RAM 124, more specifically, tothe word of row 6 whose index mod 4 equals 3 within the four columnscorresponding to a NPU group 4901, for use in the next time step (i.e.,by the instruction at address 12 during the next loop iteration).Additionally, each execution instance of the instruction at address 14writes the tanh(C) value to OUTBUF[3], which will be used by theinstruction at address 15.

More specifically, the multiply-accumulate instruction at address 11(MULT-ACCUM OUTBUF[0], OUTBUF[3]) reads the input gate (I) value fromOUTBUF[0] and reads the candidate cell state (C′) value from OUTBUF[3]and multiplies them to generate a first product added to the accumulator202, which was cleared by the instruction at address 10. Morespecifically, each of the four NPUs 126 within an NPU group 4901computes the first product of I and C′.

The multiply-accumulate instruction at address 12 (MULT-ACCUM OUTBUF[1],WR ROW 6) instructs the NPUs 126 to read the forget gate (F) value fromOUTBUF[1] and to read its respective word from row 6 of the weight RAM124 and multiplies them to generate a second product added to the firstproduct in the accumulator 202 generated by the instruction at address11. More specifically, the word read from row 6 is the current cellstate (C) value computed in the previous time step in the case of theNPU 126 of the NPU group 4901 whose index mod 4 equals 3 such that thesum of the first and second products is the new cell state (C). However,the words read from row 6 are don't-care values for the other three NPUs126 of the NPU group 4901 since their resulting accumulated values willnot be used, i.e., will not be put into the output buffer 1104 by theinstructions at addresses 13 and 14 and will be cleared by theinstruction at address 14. That is, only the resulting new cell state(C) value generated by the NPU 126 of the NPU group 4901 whose index mod4 equals 3 will be used, namely per the instructions at addresses 13 and14. In the case of the second through thirty-first execution instancesof the instruction at address 12, the C value read from row 6 of theweight RAM 124 was written by the instruction at address 13 during theprevious iteration of the loop body. However, for the first executioninstance of the instruction at address 12, the C values in row 6 arewritten with initial values, either by the architectural program priorto starting the non-architectural program of FIG. 51 or by a modifiedversion of the non-architectural program.

The output instruction at address 13 (OUTPUT PASSTHRU, WR ROW 6,MASK[0:2]) passes through the accumulator 202 value, i.e., the computedC value, only to OUTBUF[3] (i.e., only the NPU 126 of the NPU group 4901whose index mod 4 equals 3 writes its computed C value to the outputbuffer 1104) and row 6 of the weight RAM 124 is written with the updatedoutput buffer 1104, as shown in FIG. 50. That is, the output instructionat address 13 masks OUTBUF[0], OUTBUF[1] and OUTBUF[2] to cause them toretain their current values (which are I, F, and O). As described above,only the C value in the word of row 6 within each group of four columnscorresponding to a NPU group 4901 whose index mod 4 equals 3 is used,namely by the instruction at address 12; thus, the non-architecturalprogram does not care about the values in columns 0-2, 4-6, and so forthto 508-510 of row 6 of the weight RAM 124, as shown in FIG. 50 (whichare the I, F, and O values).

The output instruction at address 14 (OUTPUT TANH, NOP, MASK[0:2], CLRACC) performs a tanh activation function on the accumulator 202 valueand writes the computed tanh(C) values to OUTBUF[3], and clears theaccumulator 202, without writing to memory. The output instruction ataddress 14, like the output instruction at address 13, masks OUTBUF[0],OUTBUF[1], and OUTBUF[2] to cause them to retain their current values,as shown in FIG. 50.

Each execution instance of the instructions at addresses 15 through 16computes and writes the cell output (H) values generated by the currenttime step to the second next row after the current output row of thedata RAM 122, which will be read by the architectural program and usedin the next time step (i.e., by the instructions at addresses 3 and 7during the next loop iteration). More specifically, themultiply-accumulate instruction at address 15 reads the output gate (O)value from OUTBUF[2] and reads the tanh(C) value from OUTBUF[3] andmultiplies them to generate a product added to the accumulator 202,which was cleared by the instruction at address 14. More specifically,each of the four NPUs 126 within an NPU group 4901 computes the productof O and tanh(C).

The output instruction at address 16 passes through the accumulator 202value and writes the computed H values to row 3 during the firstexecution instance, to row 5 during the second execution instance, andso forth to row 63 during the thirty-first execution instance, as shownin FIG. 50, which are subsequently used by the instructions at addresses4 and 8. Additionally, the computed H values are put into the outputbuffer 1104, as shown in FIG. 50, for subsequent use by the instructionsat addresses 4 and 8. The output instruction at address 16 also clearsthe accumulator 202. In one embodiment, the LSTM cell 4600 is designedsuch that the output instruction at address 16 (and/or the outputinstruction at address 22 of FIG. 48) has an activation function, e.g.,sigmoid or tanh, rather than passing through the accumulator 202 value.

The loop instruction at address 17 decrements the loop counter 3804 andloops back to the instruction at address 3 if the new the loop counter3804 value is greater than zero.

As may be observed, the number of instructions in the loop body of thenon-architectural program of FIG. 51 is approximately 34% less than thatof the non-architectural of FIG. 48, which is facilitated by the outputbuffer 1104 feedback and masking capability of the NNU 121 embodiment ofFIG. 49. Additionally, the memory layout in the data RAM 122 of thenon-architectural program of FIG. 51 accommodates approximately threetimes the number of time steps as that of FIG. 48, which is alsofacilitated by the output buffer 1104 feedback and masking capability ofthe NNU 121 embodiment of FIG. 49. Depending upon the particulararchitectural program application employing the NNU 121 to perform LSTMcell layer computations, these improvements may be helpful, particularlyin applications in which the number of LSTM cells 4600 in an LSTM layeris less than or equal to 128.

In the embodiment of FIGS. 47 through 51, it is assumed the weight andbias values remain the same across time steps. However, otherembodiments are contemplated in which the weight and bias values varyacross time steps in which case rather than the weight RAM 124 beingpopulated with a single set of the weight and bias values as shown inFIGS. 47 and 50, the weight RAM 124 is populated with a different set ofthe weight and bias values for each time step and the weight RAM 124addresses of the non-architectural programs of FIGS. 48 and 51 aremodified accordingly.

The embodiments of FIGS. 47 through 51 have been described in which,generally speaking, the weight, bias and intermediate values (e.g., C,C′) are stored in the weight RAM 124 and the input and output values(e.g., X, H) are stored in the data RAM 122. This may be advantageousfor embodiments in which the data RAM 122 is dual-ported and the weightRAM 124 is single-ported since there is more traffic from thenon-architectural and architectural programs to the data RAM 122.However, since the weight RAM 124 is larger, embodiments arecontemplated in which the non-architectural and architectural programsare written to swap the memories (i.e., the data RAM 122 and weight RAM124) in which the values are stored. That is, the W, U, B, C′, tanh(C)and C values are stored in the data RAM 122 and the X, H, I, F and Ovalues are stored in the weight RAM 124 (modified embodiment of FIG.47); and the W, U, B, C values are stored in the data RAM 122 and the Xand H values are stored in the weight RAM 124 (modified embodiment ofFIG. 50). For these embodiments, a larger number of time steps may beprocessed together in a batch since the weight RAM 124 is larger. Thismay be advantageous for some architectural program application makinguse of the NNU 121 to perform computations that benefit from the largernumber of time steps and for which a single-ported memory (e.g., theweight RAM 124) provides sufficient bandwidth.

Referring now to FIG. 52, a block diagram illustrating an NNU 121embodiment with output buffer masking and feedback capability within NPUgroups and which employs shared AFUs 1112 is shown. The NNU 121 of FIG.52 is similar in many respects to the NNU 121 of FIG. 49 andlike-numbered elements are similar. However, the four AFUs 212 of FIG.49 are replaced by a single shared AFU 1112 that receives the fouroutputs 217 of the four accumulators 202 and generates four outputs toOUTBUF[0], OUTBUF[1], OUTBUF[2], and OUTBUF[3]. The NNU 121 of FIG. 52operates in a manner similar to that described above with respect toFIGS. 49 through 51 and similar to the manner described above withrespect to Figure FIGS. 11 through 13 with respect to operation of theshared AFU 1112.

Referring now to FIG. 53, a block diagram illustrating an example of thelayout of data within the data RAM 122, weight RAM 124 and output buffer1104 of the NNU 121 of FIG. 49 as it performs calculations associatedwith a layer of 128 LSTM cells 4600 of FIG. 46 according to an alternateembodiment is shown. The example of FIG. 53 is similar in many respectsto the example of FIG. 50. However, in FIG. 53, the Wi, Wf and Wo valuesare in row 0 (rather than in row 3 as in FIG. 50); the Ui, Uf and Uovalues are in row 1 (rather than in row 4 as in FIG. 50); the Bi, Bf andBo values are in row 2 (rather than in row 5 as in FIG. 50); and the Cvalues are in row 3 (rather than in row 6 as in FIG. 50). Additionally,the output buffer 1104 contents are the same in FIG. 53 as in FIG. 50,however, the contents of the third row (i.e., the I, F, O and C′ values)are present in the output buffer 1104 after execution of the instructionat 7 (rather than 10 in FIG. 50); the contents of the fourth row (i.e.,the I, F, O and C values) are present in the output buffer 1104 afterexecution of the instruction at 10 (rather than 13 in FIG. 50); thecontents of the fifth row (i.e., the I, F, O and tanh(C) values) arepresent in the output buffer 1104 after execution of the instruction at11 (rather than 14 in FIG. 50); and the contents of the sixth row (i.e.,the H values) are present in the output buffer 1104 after execution ofthe instruction at 13 (rather than 16 in FIG. 50), due to thedifferences in the non-architectural program of FIG. 54 from that ofFIG. 51, which are described in more detail below.

Referring now to FIG. 54, a table illustrating a program for storage inthe program memory 129 of and execution by the NNU 121 of FIG. 49 toaccomplish computations associated with an LSTM cell layer and usingdata and weights according to the arrangement of FIG. 53 is shown. Theexample program of FIG. 54 is similar in many ways to the program ofFIG. 51. More specifically, the instructions at addresses 0 through 5are the same in FIGS. 54 and 51; the instructions at address 7 and 8 ofFIG. 54 are the same as the instructions at address 10 and 11 of FIG.51; and the instructions at addresses 10 through 14 of FIG. 54 are thesame as the instructions at addresses 13 through 17 of FIG. 51.

However, the instruction at address 6 of FIG. 54 does not clear theaccumulator 202 (whereas the instruction at address 6 of FIG. 51 does).Furthermore, the instructions at addresses 7 through 9 are not presentin the non-architectural of FIG. 54. Finally, the instruction at address9 of FIG. 54 is the same as the instruction at address 12 of FIG. 51except that the instruction at address 9 of FIG. 54 reads from row 3 ofthe weight RAM 124, whereas, the instruction at address 12 of FIG. 51reads from row 6 of the weight RAM 124.

As a result of the differences between the non-architectural programs ofFIGS. 54 and 51, the layout of FIG. 53 uses three less rows of weightRAM 124 and includes three fewer instructions in the program loop.Indeed, the size of the loop body of the non-architectural program ofFIG. 54 is essentially half the size of the loop body of thenon-architectural program of FIG. 48 and approximately 80% the size ofthe loop body of the non-architectural program of FIG. 51.

Referring now to FIG. 55, a block diagram illustrating portions of anNPU 126 according to an alternate embodiment are shown. Morespecifically, for a single NPU 126 of the NPUs 126 of FIG. 49, themux-reg 208 and its associated inputs 207, 211, and 4905, and themux-reg 705 its associated inputs 206, 711, and 4907 are shown. Inaddition to the inputs of FIG. 49, the mux-reg 208 and the mux-reg 705of the NPU 126 each receive an index_within_group input 5599. The indexwithin group input 5599 indicates the index of the particular NPU 126within its NPU group 4901. Thus, for example, in an embodiment in whicheach NPU group 4901 has four NPUs 126, within each NPU group 4901, oneof the NPUs 126 receives a value of zero on its index_within_group input5599, one of the NPUs 126 receives a value of one on itsindex_within_group input 5599, one of the NPUs 126 receives a value oftwo on its index_within_group input 5599, and one of the NPUs 126receives a value of three on its index_within_group input 5599. Statedalternatively, the index_within_group input 5599 value received by anNPU 126 is its index within the NNU 121 mod J, where J is the number ofNPUs 126 in an NPU group 4901. Thus, for example, NPU 73 receives avalue of one on its index_within_group input 5599, NPU 353 receives avalue of three on its index_within_group input 5599, and NPU 6 receivesa value of two on its index_within_group input 5599.

Additionally, when the control input 213 specifies a predeterminedvalue, referred to herein as “SELF,” the mux-reg 208 selects the outputbuffer 1104 input 4905 corresponding to the index_within_group input5599 value. Thus, advantageously, when a non-architectural instructionspecifies to receive data from the output buffer 1104 with a value ofSELF (denoted OUTBUF[SELF] in the instructions at addresses 2 and 7 ofFIG. 57), the mux-reg 208 of each NPU 126 receives its correspondingword from the output buffer 1104. Thus, for example, when the NNU 121executes the non-architectural instruction at addresses 2 and 7 of FIG.57, the mux-reg 208 of NPU 73 selects the second (index 1) of the fourinputs 4905 to receive word 73 from the output buffer 1104, the mux-reg208 of NPU 353 selects the fourth (index 3) of the four inputs 4905 toreceive word 353 from the output buffer 1104, and the mux-reg 208 of NPU6 selects the third (index 2) of the four inputs 4905 to receive word 6from the output buffer 1104. Although not employed in thenon-architectural program of FIG. 57, a non-architectural instructionmay specify to receive data from the output buffer 1104 with a value ofSELF (OUTBUF[SELF]) to cause the control input 713 to specify thepredetermined value to cause the mux-reg 705 of each NPU 126 to receiveits corresponding word from the output buffer 1104.

Referring now to FIG. 56, a block diagram illustrating an example of thelayout of data within the data RAM 122 and weight RAM 124 of the NNU 121as it performs calculations associated with the Jordan RNN of FIG. 43but employing the benefits afforded by the embodiments of FIG. 55 isshown. The layout of the weights within the weight RAM 124 is the sameas that of FIG. 44. The layout of the values within the data RAM 122 issimilar to that of FIG. 44, except that each time step has an associatedpair of rows that hold input layer node D values and output layer node Yvalues, rather than a quadruplet of rows as in FIG. 44. That is, thehidden layer Z and context layer C values are not written to the dataRAM 122. Rather, the output buffer 1104 is used as a scratchpad for thehidden layer Z and context layer C values, as described in more detailwith respect to the non-architectural program of FIG. 57.Advantageously, the OUTBUF[SELF] output buffer 1104 feedback featurepotentially enables the non-architectural program to be faster (due tothe replacement of two writes and two reads from the data RAM 122 withtwo writes and two reads from the output buffer 1104) and enables eachtime step to use less data RAM 122 space, which enables the data RAM 122to hold approximately twice as many time steps as the embodiment ofFIGS. 44 and 45, in particular 32 time steps, as shown.

Referring now to FIG. 57, a table illustrating a program for storage inthe program memory 129 of and execution by the NNU 121 to accomplish aJordan RNN and using data and weights according to the arrangement ofFIG. 56 is shown. The non-architectural program of FIG. 57 is similar insome respects to the non-architectural of FIG. 45, and differences aredescribed.

The example program of FIG. 57 includes 12 non-architecturalinstructions at addresses 0 through 11. The initialize instruction ataddress 0 clears the accumulator 202 and initializes the loop counter3804 to a value of 32 to cause the loop body (the instructions ofaddresses 2 through 11) to be performed 32 times. The output instructionat address 1 puts the zero values of the accumulator 202 (cleared by theinitialize instruction at address 0) into the output buffer 1104. As maybe observed, the 512 NPUs 126 correspond to and operate as the 512hidden layer nodes Z during the execution of the instructions ofaddresses 2 through 6, and correspond to and operate as the 512 outputlayer nodes Y during the execution of the instructions of addresses 7through 10. That is, the 32 execution instances of the instructions ataddresses 2 through 6 compute the value of the hidden layer nodes Z forthe 32 corresponding time steps and put them into the output buffer 1104to be used by the corresponding 32 execution instances of theinstructions at addresses 7 through 9 to calculate and write to the dataRAM 122 the output layer nodes Y of the corresponding 32 time steps andto be used by the corresponding 32 execution instances of theinstructions at address 10 to put the context layer nodes C of thecorresponding 32 time steps in the output buffer 1104. (The contextlayer nodes C of the thirty-second time step put into the output buffer1104 is not used.)

During the first execution instance of the instructions at addresses 2and 3 (ADD_D_ACC OUTBUF[SELF] and ADD_D_ACC ROTATE, COUNT=511), each ofthe 512 NPUs 126 accumulates into its accumulator 202 the 512 contextnode C values of the output buffer 1104, which were generated andwritten by the execution of the instructions of addresses 0 through 1.During the second and subsequent execution instances of the instructionsat addresses 2 and 3, each of the 512 NPUs 126 accumulates into itsaccumulator 202 the 512 context node C values of the output buffer 1104,which were generated and written by the execution of the instructions ofaddresses 7 through 8 and 10. More specifically, the instruction ataddress 2 instructs the mux-reg 208 of each NPU 126 to select itscorresponding the output buffer 1104 word, as described above, and toadd it to the accumulator 202; the instruction at address 3 instructsthe NPU 126 to rotate the context node C values in the 512-word rotatercollectively formed by the connected mux-regs 208 of the 512 NPUs 126among the 512 NPUs 126 to enable each NPU 126 to accumulate the 512context node C values into its accumulator 202. The instruction ataddress 3 does not clear the accumulator 202, which enables theinstructions at addresses 4 and 5 to accumulate the input layer nodes D(multiplied by their corresponding weights) with the context node Cvalues accumulated by execution of the instructions at addresses 2 and3.

During each execution instance of the instructions at addresses 4 and 5(MULT-ACCUM DR ROW+2, WR ROW 0 and MULT-ACCUM ROTATE, WR ROW+1,COUNT=511), each NPU 126 of the 512 NPUs 126 performs 512 multiplyoperations of the 512 input node D values in the row of the data RAM 122associated with the current time step (e.g., row 0 for time step 0, row2 for time step 1, and so forth to row 62 for time step 31) by the NPU's126 respective column of weights from rows 0 through 511 of the weightRAM 124 to generate 512 products that, along with the accumulation ofthe 512 context C node values performed by the instructions at addresses2 and 3, are accumulated into the accumulator 202 of the respective NPU126 to compute the hidden node Z layer values.

During each execution of the instruction at address 6 (OUTPUT PASSTHRU,NOP, CLR ACC), the 512 accumulator 202 values of the 512 NPUs 126 arepassed through and written to their respective words of the outputbuffer 1104, and the accumulator 202 is cleared.

During each execution instance of the instructions at addresses 7 and 8(MULT-ACCUM OUTBUF[SELF], WR ROW 512 and MULT-ACCUM ROTATE, WR ROW+1,COUNT=511), each NPU 126 of the 512 NPUs 126 performs 512 multiplyoperations of the 512 hidden node Z values in the output buffer 1104(which were generated and written by the corresponding executioninstance of the instructions at addresses 2 through 6) by the NPU's 126respective column of weights from rows 512 through 1023 of the weightRAM 124 to generate 512 products that are accumulated into theaccumulator 202 of the respective NPU 126.

During the each execution instance of the instruction at address 9(OUTPUT ACTIVATION FUNCTION, DR OUT ROW+2), an activation function(e.g., tanh, sigmoid, rectify) is performed on the 512 accumulatedvalues to compute the output node Y layer values that are written to therow of the data RAM 122 associated with the current time stamp (e.g.,row 1 for time step 0, row 3 for time step 1, and so forth to row 63 fortime step 31). The output instruction at address 9 does not clear theaccumulator 202.

During the each execution instance of the instruction at address 10(OUTPUT PASSTHRU, NOP, CLR ACC), the 512 values accumulated by theinstructions at addresses 7 and 8 are put into the output buffer 1104for use by the next execution instance of the instructions at addresses2 and 3, and the accumulator 202 is cleared.

The loop instruction at address 11 decrements the loop counter 3804 andloops back to the instruction at address 2 if the new the loop counter3804 value is greater than zero.

As described with respect to FIG. 44, in the example Jordan RNNperformed by the non-architectural program of FIG. 57, although anactivation function is applied to the accumulator 202 values to generatethe output layer node Y values, it is assumed that the accumulator 202values prior to the application of the activation function are passedthrough to the context layer nodes C rather than the actual output layernode Y values. However, for a Jordan RNN in which an activation functionis applied to the accumulator 202 values to generate the context layernodes C, the instruction at address 10 would be eliminated from thenon-architectural program of FIG. 57. Although embodiments have beendescribed in which an Elman or Jordan RNN includes a single hidden nodelayer (e.g., FIGS. 40 and 42), it should be understood that embodimentsof the processor 100 and NNU 121 are configured to efficiently performthe computations associated with an RNN that includes multiple hiddenlayers in manners similar to those described herein.

As described with respect to FIG. 2 above, advantageously each NPU 126is configured to operate as a neuron in an artificial neural network,and all the NPUs 126 of the NNU 121 operate in a massively parallelfashion to efficiently compute the neuron output values for a layer ofthe network. The parallel fashion in which the NNU operates, inparticular by employing the collective NPU mux-reg rotater, is perhapscounter-intuitive to the conventional manner of computing neuron layeroutput values. More specifically, the conventional manner typicallyinvolves performing the computations associated with a single neuron, ora relatively small subset of neurons, (e.g., using parallel arithmeticunits to perform the multiplies and adds), then moving on to performingthe computations associated with the next neuron in the layer, and soforth in a serial fashion until the computations have been performed forall the neurons in the layer. In contrast, each clock cycle all the NPUs126 (neurons) of the NNU 121 in parallel perform a small subset of thecomputations (e.g., a single multiply and accumulate) associated withthe generation of all the neuron outputs. Advantageously, by the end ofthe approximately M clock cycles—where M is the number of nodesconnected in to the current layer—the NNU 121 has computed the output ofall the neurons. For many artificial neural network configurations, dueto the large number of NPUs 126, the NNU 121 may be able to compute theneuron output values for all the neurons of the entire layer in by theend of the M clock cycles. As may be observed from the descriptionsherein, this computation efficiency is useful for all sorts ofartificial neural network computations, including but not limited tofeed-forward and recurrent neural networks, such as Elman, Jordan andLSTM networks. Finally, although embodiments are described in which theNNU 121 is configured as 512 NPUs 126 (e.g., in a wide wordconfiguration) to perform recurrent neural network computations, otherembodiments are contemplated in which the NNU 121 is configured as 1024NPUs 126 (e.g., in a narrow word configuration) to perform recurrentneural network computations and, as described above, embodiments of theNNU 121 are contemplated having different numbers of NPUs 126 than 512or 1024.

While various embodiments of the present invention have been describedherein, it should be understood that they have been presented by way ofexample, and not limitation. It will be apparent to persons skilled inthe relevant computer arts that various changes in form and detail canbe made therein without departing from the scope of the invention. Forexample, software can enable, for example, the function, fabrication,modeling, simulation, description and/or testing of the apparatus andmethods described herein. This can be accomplished through the use ofgeneral programming languages (e.g., C, C++), hardware descriptionlanguages (HDL) including Verilog HDL, VHDL, and so on, or otheravailable programs. Such software can be disposed in any known computerusable medium such as magnetic tape, semiconductor, magnetic disk, oroptical disc (e.g., CD-ROM, DVD-ROM, etc.), a network, wire line,wireless or other communications medium. Embodiments of the apparatusand method described herein may be included in a semiconductorintellectual property core, such as a processor core (e.g., embodied, orspecified, in a HDL) and transformed to hardware in the production ofintegrated circuits. Additionally, the apparatus and methods describedherein may be embodied as a combination of hardware and software. Thus,the present invention should not be limited by any of the exemplaryembodiments described herein, but should be defined only in accordancewith the following claims and their equivalents. Specifically, thepresent invention may be implemented within a processor device that maybe used in a general-purpose computer. Finally, those skilled in the artshould appreciate that they can readily use the disclosed conception andspecific embodiments as a basis for designing or modifying otherstructures for carrying out the same purposes of the present inventionwithout departing from the scope of the invention as defined by theappended claims.

1. A neural network unit (NNU) that performs calculations for arecurrent neural network (RNN) having input layer nodes, hidden layernodes, output layer nodes and context layer nodes, the NNU comprising:an array of neural processing units (NPU), at least one random accessmemory (RAM), and an output buffer, the array of NPUs: (a) read, fromthe output buffer, values of the context layer nodes associated with afirst time step; (b) read, from the RAM, values of the input layer nodesassociated with a second time step subsequent to the first time step;(c) generate values of the hidden layer nodes associated with the secondtime step based on the values of the input layer nodes read from the RAMand the values of the context layer nodes read from the output buffer;(d) output the hidden layer node values associated with the second timestep to the output buffer rather than to the RAM; (e) read, from theoutput buffer, the hidden layer node values associated with the secondtime step; (f) generate values of the context layer nodes associatedwith the second time step based on the hidden layer node values readfrom the output buffer; (g) output the context layer node valuesassociated with the second time step to the output buffer rather than tothe RAM; (h) generate values of the output layer nodes associated withthe second time step using the hidden layer node values associated withthe second time step; (i) write the output layer node values associatedwith the second time step to the RAM; and (j) repeat (a) through (i) fora sequence of time steps.
 2. The NNU of claim 1, further comprising: thearray of NPUs comprises N NPUs, each comprising a multiplexed register,an arithmetic unit, and an accumulator; the arithmetic unit receives anoutput of the multiplexed register and an output of the accumulator, andthe arithmetic unit generates a result provided to the accumulator; theoutput buffer is N words wide and is configured to hold N of thecontext/hidden layer node values; and the N multiplexed registers arearranged to form an N-word hardware rotater that receives the N words ofthe output buffer.
 3. The NNU of claim 2, further comprising: to said(e) read, from the output buffer, the hidden layer node valuesassociated with the second time step, the N NPUs read the N values ofthe hidden layer nodes from the output buffer into the rotater; the NNPUs read, from the RAM, weight values associated with connectionsbetween the hidden layer nodes and the output layer nodes; and to said(f) generate values of the context layer nodes associated with thesecond time step based on the hidden layer node values read from theoutput buffer, the N NPUs: rotate the N values of the hidden layer nodesthrough the rotater for provision to the arithmetic unit of each of theN NPUs; multiply, by each of the N arithmetic units, each of the Nrotated hidden layer node values by one of the weight values to generateN respective products; and accumulate, into each of the N accumulators,the N respective products to generate a result.
 4. The NNU of claim 3,further comprising: a plurality of activation function units (AFU) thatreceive the accumulator output of associated one or more of the NPUs andperform an activation function on the accumulator output.
 5. The NNU ofclaim 4, further comprising: to said (h) generate values of the outputlayer nodes associated with the second time step using the hidden layernode values associated with the second time step, the plurality of AFUs:for each result of the N results of the accumulated respective Nrespective products received from the accumulator output of each of theN NPUs, perform an activation function on the result to generate arespective output layer node value.
 6. The NNU of claim 4, furthercomprising: the plurality of AFUs is N/J AFUs, and each AFU of the N/JAFUs is shared by a respective group of J of the N NPUs; and each of theN/J AFUs is configured to provide its result of the activation functionto a respective group of J words of the output buffer.
 7. The NNU ofclaim 4, further comprising: the plurality of AFUs is N, and each of theN AFUs is coupled to receive the accumulator output of a respective oneof the N NPUs and to provide its result of the activation function to arespective one of the N words of the output buffer.
 8. The NNU of claim2, further comprising: to said (a) read, from the output buffer, valuesof the context layer nodes associated with a first time step, the N NPUsread the N values of the context layer nodes from the output buffer intothe rotater; and to said (c) generate values of the hidden layer nodesassociated with the second time step based on the values of the inputlayer nodes read from the RAM and the values of the context layer nodesread from the output buffer, the N NPUs: rotate the N values of thecontext layer nodes through the rotater for provision to the arithmeticunit of each of the N NPUs; and accumulate, into each of the Naccumulators, the N values of the context layer nodes.
 9. The NNU ofclaim 8, further comprising: to said (b) read, from the RAM, values ofthe input layer nodes associated with a second time step subsequent tothe first time step, the N NPUs read the N values of the input layernodes nodes from the RAM into the rotater; the N NPUs read, from theRAM, weight values associated with connections between the input layernodes and the hidden layer nodes; and to said (c) generate values of thehidden layer nodes associated with the second time step based on thevalues of the input layer nodes read from the RAM and the values of thecontext layer nodes read from the output buffer, the N NPUs further:rotate the N values of the input layer nodes through the rotater forprovision to the arithmetic unit of each of the N NPUs; multiply, byeach of the N arithmetic units, each of the N rotated input layer nodevalues by one of the weight values to generate N respective products;and accumulate, into each of the N accumulators, the N respectiveproducts along with the accumulated N values of the context layer nodes.10. The NNU of claim 1, further comprising: a program memory that holdsinstructions of a non-architectural program; a sequencer that fetchesthe non-architectural program instructions from the program memory andgenerates micro-operations to control the array of NPUs to perform (a)through (j).
 11. The NNU of claim 10, further comprising: the NNU iscomprised in a processor that fetches and executes instructions of anarchitectural program of the processor.
 12. The NNU of claim 11, furthercomprising: the output buffer is accessible by the non-architecturalprogram and is not accessible by the architectural program.
 13. The NNUof claim 11, further comprising: the at least one memory is accessibleby the architectural program to write the values of the input layernodes associated with the sequence of time steps and to read the valuesof the output layer nodes associated with the sequence of time steps;and the program memory is accessible by the architectural program towrite the non-architectural program to the program memory.
 14. A methodfor operating a neural network unit (NNU) that performs calculations fora recurrent neural network (RNN) having input layer nodes, hidden layernodes, output layer nodes and context layer nodes, the NNU having anarray of neural processing units (NPU), at least one random accessmemory (RAM), and an output buffer, the method comprising: (a) reading,from the output buffer, values of the context layer nodes associatedwith a first time step; (b) reading, from the RAM, values of the inputlayer nodes associated with a second time step subsequent to the firsttime step; (c) generating values of the hidden layer nodes associatedwith the second time step based on the values of the input layer nodesread from the RAM and the values of the context layer nodes read fromthe output buffer; (d) outputting the hidden layer node valuesassociated with the second time step to the output buffer rather than tothe RAM; (e) reading, from the output buffer, the hidden layer nodevalues associated with the second time step; (f) generating values ofthe context layer nodes associated with the second time step based onthe hidden layer node values read from the output buffer; (g) outputtingthe context layer node values associated with the second time step tothe output buffer rather than to the RAM; (h) generating values of theoutput layer nodes associated with the second time step using the hiddenlayer node values associated with the second time step; (i) writing theoutput layer node values associated with the second time step to theRAM; and (j) repeating (a) through (i) for a sequence of time steps. 15.The method of claim 14, further comprising: the array of NPUs comprisesN NPUs, each comprising a multiplexed register, an arithmetic unit, andan accumulator; the arithmetic unit receives an output of themultiplexed register and an output of the accumulator, and thearithmetic unit generates a result provided to the accumulator; theoutput buffer is N words wide and is configured to hold N of thecontext/hidden layer node values; the N multiplexed registers arearranged to form an N-word hardware rotater that receives the N words ofthe output buffer; said (e) reading, from the output buffer, the hiddenlayer node values associated with the second time step comprises:reading, by the N NPUs, the N values of the hidden layer nodes from theoutput buffer into the rotater; reading, by the N NPUs from the RAM,weight values associated with connections between the hidden layer nodesand the output layer nodes; and said (f) generating values of thecontext layer nodes associated with the second time step based on thehidden layer node values read from the output buffer comprises: by the NNPUs: rotating the N values of the hidden layer nodes through therotater for provision to the arithmetic unit of each of the N NPUs;multiplying, by each of the N arithmetic units, each of the N rotatedhidden layer node values by one of the weight values to generate Nrespective products; and accumulating, into each of the N accumulators,the N respective products to generate a result.
 16. The method of claim15, further comprising: the apparatus includes a plurality of activationfunction units (AFU) that receive the accumulator output of associatedone or more of the NPUs and perform an activation function on theaccumulator output.
 17. The method of claim 16, further comprising: said(h) generating values of the output layer nodes associated with thesecond time step using the hidden layer node values associated with thesecond time step, comprises, by the plurality of AFUs: for each resultof the N results of the accumulated respective N respective productsreceived from the accumulator output of each of the N NPUs, performingan activation function on the result to generate a respective outputlayer node value.
 18. The method of claim 15, further comprising: said(a) reading, from the output buffer, values of the context layer nodesassociated with a first time step, comprises: reading, by the N NPUs,the N values of the context layer nodes from the output buffer into therotater; and said (c) generating values of the hidden layer nodesassociated with the second time step based on the values of the inputlayer nodes read from the RAM and the values of the context layer nodesread from the output buffer comprises: by the N NPUs: rotating the Nvalues of the context layer nodes through the rotater for provision tothe arithmetic unit of each of the N NPUs; and accumulating, into eachof the N accumulators, the N values of the context layer nodes.
 19. Acomputer program product encoded in at least one non-transitory computerusable medium for use with a computing device, the computer programproduct comprising: computer usable program code embodied in saidmedium, for specifying a neural network unit (NNU) that performscalculations for a recurrent neural network (RNN) having input layernodes, hidden layer nodes, output layer nodes and context layer nodes,the computer usable program code comprising: first program code forspecifying an array of neural processing units (NPU), at least onerandom access memory (RAM), and an output buffer; and the array of NPUs:(a) read, from the output buffer, values of the context layer nodesassociated with a first time step; (b) read, from the RAM, values of theinput layer nodes associated with a second time step subsequent to thefirst time step; (c) generate values of the hidden layer nodesassociated with the second time step based on the values of the inputlayer nodes read from the RAM and the values of the context layer nodesread from the output buffer; (d) output the hidden layer node valuesassociated with the second time step to the output buffer rather than tothe RAM; (e) read, from the output buffer, the hidden layer node valuesassociated with the second time step; (f) generate values of the contextlayer nodes associated with the second time step based on the hiddenlayer node values read from the output buffer; (g) output the contextlayer node values associated with the second time step to the outputbuffer rather than to the RAM; (h) generate values of the output layernodes associated with the second time step using the hidden layer nodevalues associated with the second time step; (i) write the output layernode values associated with the second time step to the RAM; and (j)repeat (a) through (i) for a sequence of time steps.
 20. The computerprogram product of claim 19, further comprising: the array of NPUscomprises N NPUs, each comprising a multiplexed register, an arithmeticunit, and an accumulator; the arithmetic unit receives an output of themultiplexed register and an output of the accumulator, and thearithmetic unit generates a result provided to the accumulator; theoutput buffer is N words wide and is configured to hold N of thecontext/hidden layer node values; and the N multiplexed registers arearranged to form an N-word hardware rotater that receives the N words ofthe output buffer.